THE EFFECT OF ANTAGONIST CONDITIONING CONTRACTIONS ON LOWER AND UPPER BODY POWER TESTS

This study assessed the effect of antagonist conditioning contractions (ACC) on lower and upper body power tests. Six subjects performed the bilateral countermovement jump and the supine medicine ball chest throw on a force platform in baseline conditions and after ACC. A repeated measures ANOVA was used to compare performance between the baseline conditions and the ACC condition. Analysis demonstrated no significant main effects for GRF (p = 0.41) or RFD (p = 0.55) for the countermovement jump. Additionally, there were no significant main effects for GRF (p = 0.85) or RFD (p = 0.95) for the medicine ball throw. This study demonstrated that maximal short term ACC do not enhance multi-joint power tests such as the countermovement jump and medicine ball throw

THE OSTEOGENIC POTENTIAL OF SUPERMAXIMAL SQUAT LOADS

This study evaluated the ground reaction force (GRF) and rate of force development (RFD) of the back squat at 3 different loads. Twelve subjects performed the back squat with 80%, 100%, and 120% of their 1 repetition maximum (RM) on a force platform. A two way repeated measures ANOVA revealed significant main effects for GRF for both the eccentric (p &#8804; 0.001) and concentric (p &#8804; 0.001) phases but no interaction between phase and GRF or RFD (p < 0.05). No significant main effects were found for RFD for the eccentric (p = 0.09) and concentric phases (p = 0.38). Post hoc analyses demonstrated that back squats at 120% produced the highest GRF in the eccentric and concentric conditions. Mean RFD was highest, and trending toward significance, during the eccentric phase at 100% of 1 RM condition

The summertime Boreal forest field measurement intensive (HUMPPA-COPEC-2010): an overview of meteorological and chemical influences

This paper describes the background, instrumentation, goals, and the regional influences on the HUMPPA-COPEC intensive field measurement campaign, conducted at the Boreal forest research station SMEAR II (Station for Measuring Ecosystem-Atmosphere Relation) in Hyytiälä, Finland from 12 July–12 August 2010. The prevailing meteorological conditions during the campaign are examined and contrasted with those of the past six years. Back trajectory analyses show that meteorological conditions at the site in 2010 were characterized by a higher proportion of southerly flow than in the other years studied. As a result the summer of 2010 was anomalously warm and high in ozone making the campaign relevant for the analysis of possible future climates. A comprehensive land use analysis, provided on both 5 and 50 km scales, shows that the main vegetation types surrounding the site on both the regional and local scales are: coniferous forest (Scots pine and/or Norway spruce); mixed forest (Birch and conifers); and woodland scrub (e.g. Willows, Aspen); indicating that the campaign results can be taken as representative of the Boreal forest ecosystem. In addition to the influence of biogenic emissions, the measurement site was occasionally impacted by sources other than vegetation. Specific tracers have been used here to identify the time periods when such sources have impacted the site namely: biomass burning (acetonitrile and CO), urban anthropogenic pollution (pentane and SO&lt;sub&gt;2&lt;/sub&gt;) and the nearby Korkeakoski sawmill (enantiomeric ratio of chiral monoterpenes). None of these sources dominated the study period, allowing the Boreal forest summertime emissions to be assessed and contrasted with various other source signatures

Relocation to get venture capital : a resource dependence perspective

This is the author accepted manuscript. The final version is available from SAGE via the DOI in this record.Using a resource dependence perspective, we theorize and show that non-venture-capital-backed ventures founded in U.S. states with a lower availability of venture capital (VC) are more likely to relocate to California (CA) or Massachusetts (MA)—the two VC richest states—compared to ventures founded in states with a greater availability of VC. Moreover, controlling for self-selection, ventures that relocate to CA or MA subsequently have a greater probability of attracting initial VC compared to ventures that stay in their home state. We discuss the implications for theory, future research, and practice

A Hybrid Fuzzy Multi-criteria Decision Making Model to Evaluate the Overall Performance of Public Emergency Departments: A Case Study

[EN] Performance evaluation is relevant for supporting managerial decisions related to the improvement of public emergency departments (EDs). As different criteria from ED context and several alternatives need to be considered, selecting a suitable Multicriteria Decision-Making (MCDM) approach has become a crucial step for ED performance evaluation. Although some methodologies have been proposed to address this challenge, a more complete approach is still lacking. This paper bridges this gap by integrating three potent MCDM methods. First, the Fuzzy Analytic Hierarchy Process (FAHP) is used to determine the criteria and sub-criteria weights under uncertainty, followed by the interdependence evaluation via fuzzy Decision-Making Trial and Evaluation Laboratory(FDEMATEL). The fuzzy logic is merged with AHP and DEMATEL to illustrate vague judgments. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used for ranking EDs. This approach is validated in a real 3-ED cluster. The results revealed the critical role of Infrastructure (21.5%) in ED performance and the interactive nature of Patient safety (C+R =12.771). Furthermore, this paper evidences the weaknesses to be tackled for upgrading the performance of each ED.Ortiz-Barrios, M.; Alfaro Saiz, JJ. (2020). A Hybrid Fuzzy Multi-criteria Decision Making Model to Evaluate the Overall Performance of Public Emergency Departments: A Case Study. International Journal of Information Technology & Decision Making. 19(6):1485-1548. https://doi.org/10.1142/S0219622020500364S14851548196Lord, K., Parwani, V., Ulrich, A., Finn, E. B., Rothenberg, C., Emerson, B., … Venkatesh, A. K. (2018). Emergency department boarding and adverse hospitalization outcomes among patients admitted to a general medical service. The American Journal of Emergency Medicine, 36(7), 1246-1248. doi:10.1016/j.ajem.2018.03.043Sørup, C. M., Jacobsen, P., & Forberg, J. L. (2013). Evaluation of emergency department performance – a systematic review on recommended performance and quality-in-care measures. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 21(1). doi:10.1186/1757-7241-21-62Farokhi, S., & Roghanian, E. (2018). Determining quantitative targets for performance measures in the balanced scorecard method using response surface methodology. Management Decision, 56(9), 2006-2037. doi:10.1108/md-08-2017-0772Ortiz Barrios, M. A., & Felizzola Jiménez, H. (2016). Use of Six Sigma Methodology to Reduce Appointment Lead-Time in Obstetrics Outpatient Department. Journal of Medical Systems, 40(10). doi:10.1007/s10916-016-0577-3Sunder M., V., Ganesh, L. S., & Marathe, R. R. (2018). A morphological analysis of research literature on Lean Six Sigma for services. International Journal of Operations & Production Management, 38(1), 149-182. doi:10.1108/ijopm-05-2016-0273Bergeron, B. P. (2017). Performance Management in Healthcare. doi:10.4324/9781315102214Santos, S. P., Belton, V., Howick, S., & Pilkington, M. (2018). Measuring organisational performance using a mix of OR methods. Technological Forecasting and Social Change, 131, 18-30. doi:10.1016/j.techfore.2017.07.028Ho, W., & Ma, X. (2018). The state-of-the-art integrations and applications of the analytic hierarchy process. European Journal of Operational Research, 267(2), 399-414. doi:10.1016/j.ejor.2017.09.007Dargi, A., Anjomshoae, A., Galankashi, M. R., Memari, A., & Tap, M. B. M. (2014). Supplier Selection: A Fuzzy-ANP Approach. Procedia Computer Science, 31, 691-700. doi:10.1016/j.procs.2014.05.317Jing, M., Jie, Y., Shou-yi, L., & Lu, W. (2015). Application of fuzzy analytic hierarchy process in the risk assessment of dangerous small-sized reservoirs. International Journal of Machine Learning and Cybernetics, 9(1), 113-123. doi:10.1007/s13042-015-0363-4Samanlioglu, F., Taskaya, Y. E., Gulen, U. C., & Cokcan, O. (2018). A Fuzzy AHP–TOPSIS-Based Group Decision-Making Approach to IT Personnel Selection. International Journal of Fuzzy Systems, 20(5), 1576-1591. doi:10.1007/s40815-018-0474-7CHEN, M.-F., TZENG, G.-H., & TANG, T.-I. (2005). FUZZY MCDM APPROACH FOR EVALUATION OF EXPATRIATE ASSIGNMENTS. International Journal of Information Technology & Decision Making, 04(02), 277-296. doi:10.1142/s0219622005001520Gul, M., Celik, E., Gumus, A. T., & Guneri, A. F. (2016). Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis. European J. of Industrial Engineering, 10(2), 196. doi:10.1504/ejie.2016.075846Jovčić, Průša, Dobrodolac, & Švadlenka. (2019). A Proposal for a Decision-Making Tool in Third-Party Logistics (3PL) Provider Selection Based on Multi-Criteria Analysis and the Fuzzy Approach. Sustainability, 11(15), 4236. doi:10.3390/su11154236Saaty, T. L., & Vargas, L. G. (2012). Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. International Series in Operations Research & Management Science. doi:10.1007/978-1-4614-3597-6Vargas, L. G. (2016). Voting with Intensity of Preferences. International Journal of Information Technology & Decision Making, 15(04), 839-859. doi:10.1142/s0219622016400058Lee, K.-C., Tsai, W.-H., Yang, C.-H., & Lin, Y.-Z. (2018). An MCDM approach for selecting green aviation fleet program management strategies under multi-resource limitations. Journal of Air Transport Management, 68, 76-85. doi:10.1016/j.jairtraman.2017.06.011Labib, A., & Read, M. (2015). A hybrid model for learning from failures: The Hurricane Katrina disaster. Expert Systems with Applications, 42(21), 7869-7881. doi:10.1016/j.eswa.2015.06.020Hosseini, S., & Khaled, A. A. (2016). A hybrid ensemble and AHP approach for resilient supplier selection. Journal of Intelligent Manufacturing, 30(1), 207-228. doi:10.1007/s10845-016-1241-yZavadskas, E. K., Govindan, K., Antucheviciene, J., & Turskis, Z. (2016). Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues. Economic Research-Ekonomska Istraživanja, 29(1), 857-887. doi:10.1080/1331677x.2016.1237302Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Butturi, M. A., Marinello, S., & Rimini, B. (2019). On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application. Expert Systems with Applications, 120, 217-227. doi:10.1016/j.eswa.2018.11.030De Almeida Filho, A. T., Clemente, T. R. N., Morais, D. C., & de Almeida, A. T. (2018). Preference modeling experiments with surrogate weighting procedures for the PROMETHEE method. European Journal of Operational Research, 264(2), 453-461. doi:10.1016/j.ejor.2017.08.006Sun, G., Guan, X., Yi, X., & Zhou, Z. (2018). An innovative TOPSIS approach based on hesitant fuzzy correlation coefficient and its applications. Applied Soft Computing, 68, 249-267. doi:10.1016/j.asoc.2018.04.004Frazão, T. D. C., Camilo, D. G. G., Cabral, E. L. S., & Souza, R. P. (2018). Multicriteria decision analysis (MCDA) in health care: a systematic review of the main characteristics and methodological steps. BMC Medical Informatics and Decision Making, 18(1). doi:10.1186/s12911-018-0663-1Ortiz-Barrios, M. A., Herrera-Fontalvo, Z., Rúa-Muñoz, J., Ojeda-Gutiérrez, S., De Felice, F., & Petrillo, A. (2018). An integrated approach to evaluate the risk of adverse events in hospital sector. Management Decision, 56(10), 2187-2224. doi:10.1108/md-09-2017-0917Al Salem, A. A., & Awasthi, A. (2018). Investigating rank reversal in reciprocal fuzzy preference relation based on additive consistency: Causes and solutions. Computers & Industrial Engineering, 115, 573-581. doi:10.1016/j.cie.2017.11.027Aires, R. F. de F., & Ferreira, L. (2019). A new approach to avoid rank reversal cases in the TOPSIS method. Computers & Industrial Engineering, 132, 84-97. doi:10.1016/j.cie.2019.04.023Emrouznejad, A., & Yang, G. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8. doi:10.1016/j.seps.2017.01.008Arya, A., & Yadav, S. P. (2017). Development of FDEA Models to Measure the Performance Efficiencies of DMUs. International Journal of Fuzzy Systems, 20(1), 163-173. doi:10.1007/s40815-017-0325-yMufazzal, S., & Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, 119, 427-438. doi:10.1016/j.cie.2018.03.045Kaliszewski, I., & Podkopaev, D. (2016). Simple additive weighting—A metamodel for multiple criteria decision analysis methods. Expert Systems with Applications, 54, 155-161. doi:10.1016/j.eswa.2016.01.042Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2018). A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem. Journal of Cleaner Production, 182, 466-484. doi:10.1016/j.jclepro.2018.02.062Chen, Z., Ming, X., Zhang, X., Yin, D., & Sun, Z. (2019). A rough-fuzzy DEMATEL-ANP method for evaluating sustainable value requirement of product service system. Journal of Cleaner Production, 228, 485-508. doi:10.1016/j.jclepro.2019.04.145Jumaah, F. M., Zadain, A. A., Zaidan, B. B., Hamzah, A. K., & Bahbibi, R. (2018). Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment. Measurement, 118, 83-95. doi:10.1016/j.measurement.2018.01.011Singh, A., & Prasher, A. (2017). Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application. Total Quality Management & Business Excellence, 30(3-4), 284-300. doi:10.1080/14783363.2017.1302794Otay, İ., Oztaysi, B., Cevik Onar, S., & Kahraman, C. (2017). Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems, 133, 90-106. doi:10.1016/j.knosys.2017.06.028Awasthi, A., Govindan, K., & Gold, S. (2018). Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. International Journal of Production Economics, 195, 106-117. doi:10.1016/j.ijpe.2017.10.013Gul, M., Guneri, A. F., & Nasirli, S. M. (2018). A fuzzy-based model for risk assessment of routes in oil transportation. International Journal of Environmental Science and Technology, 16(8), 4671-4686. doi:10.1007/s13762-018-2078-zKazancoglu, Y., Kazancoglu, I., & Sagnak, M. (2018). Fuzzy DEMATEL-based green supply chain management performance. Industrial Management & Data Systems, 118(2), 412-431. doi:10.1108/imds-03-2017-0121Abdullah, L., & Zulkifli, N. (2015). Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management. Expert Systems with Applications, 42(9), 4397-4409. doi:10.1016/j.eswa.2015.01.021Ashtiani, M., & Azgomi, M. A. (2016). A hesitant fuzzy model of computational trust considering hesitancy, vagueness and uncertainty. Applied Soft Computing, 42, 18-37. doi:10.1016/j.asoc.2016.01.023Zyoud, S. H., & Fuchs-Hanusch, D. (2017). A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, 78, 158-181. doi:10.1016/j.eswa.2017.02.016Scholz, S., Ngoli, B., & Flessa, S. (2015). Rapid assessment of infrastructure of primary health care facilities – a relevant instrument for health care systems management. BMC Health Services Research, 15(1). doi:10.1186/s12913-015-0838-8Ivlev, I., Vacek, J., & Kneppo, P. (2015). Multi-criteria decision analysis for supporting the selection of medical devices under uncertainty. European Journal of Operational Research, 247(1), 216-228. doi:10.1016/j.ejor.2015.05.075Kovacs, E., Strobl, R., Phillips, A., Stephan, A.-J., Müller, M., Gensichen, J., & Grill, E. (2018). Systematic Review and Meta-analysis of the Effectiveness of Implementation Strategies for Non-communicable Disease Guidelines in Primary Health Care. Journal of General Internal Medicine, 33(7), 1142-1154. doi:10.1007/s11606-018-4435-5Morley, C., Unwin, M., Peterson, G. M., Stankovich, J., & Kinsman, L. (2018). Emergency department crowding: A systematic review of causes, consequences and solutions. PLOS ONE, 13(8), e0203316. doi:10.1371/journal.pone.0203316Hermann, R. M., Long, E., & Trotta, R. L. (2019). Improving Patients’ Experiences Communicating With Nurses and Providers in the Emergency Department. Journal of Emergency Nursing, 45(5), 523-530. doi:10.1016/j.jen.2018.12.001Hawley, K. L., Mazer-Amirshahi, M., Zocchi, M. S., Fox, E. R., & Pines, J. M. (2015). Longitudinal Trends in U.S. Drug Shortages for Medications Used in Emergency Departments (2001-2014). Academic Emergency Medicine, 23(1), 63-69. doi:10.1111/acem.12838Stang, A. S., Crotts, J., Johnson, D. W., Hartling, L., & Guttmann, A. (2015). Crowding Measures Associated With the Quality of Emergency Department Care: A Systematic Review. Academic Emergency Medicine, 22(6), 643-656. doi:10.1111/acem.12682Chanamool, N., & Naenna, T. (2016). Fuzzy FMEA application to improve decision-making process in an emergency department. Applied Soft Computing, 43, 441-453. doi:10.1016/j.asoc.2016.01.007Farup, P. G. (2015). Are measurements of patient safety culture and adverse events valid and reliable? Results from a cross sectional study. BMC Health Services Research, 15(1). doi:10.1186/s12913-015-0852-xCarter, E. J., Pouch, S. M., & Larson, E. L. (2013). The Relationship Between Emergency Department Crowding and Patient Outcomes: A Systematic Review. Journal of Nursing Scholarship, 46(2), 106-115. doi:10.1111/jnu.12055Ebben, R. H. A., Siqeca, F., Madsen, U. R., Vloet, L. C. M., & van Achterberg, T. (2018). Effectiveness of implementation strategies for the improvement of guideline and protocol adherence in emergency care: a systematic review. BMJ Open, 8(11), e017572. doi:10.1136/bmjopen-2017-017572Innes, G. D., Sivilotti, M. L. A., Ovens, H., McLelland, K., Dukelow, A., Kwok, E., … Chochinov, A. (2018). Emergency overcrowding and access block: A smaller problem than we think. CJEM, 21(2), 177-185. doi:10.1017/cem.2018.446Di Somma, S., Paladino, L., Vaughan, L., Lalle, I., Magrini, L., & Magnanti, M. (2014). Overcrowding in emergency department: an international issue. Internal and Emergency Medicine, 10(2), 171-175. doi:10.1007/s11739-014-1154-8Uthman, O. A., Walker, C., Lahiri, S., Jenkinson, D., Adekanmbi, V., Robertson, W., & Clarke, A. (2018). General practitioners providing non-urgent care in emergency department: a natural experiment. BMJ Open, 8(5), e019736. doi:10.1136/bmjopen-2017-019736Razzak, J. A., Baqir, S. M., Khan, U. R., Heller, D., Bhatti, J., & Hyder, A. A. (2013). Emergency and trauma care in Pakistan: a cross-sectional study of healthcare levels. Emergency Medicine Journal, 32(3), 207-213. doi:10.1136/emermed-2013-202590Dart, R. C., Goldfrank, L. R., Erstad, B. L., Huang, D. T., Todd, K. H., Weitz, J., … Anderson, V. E. (2018). Expert Consensus Guidelines for Stocking of Antidotes in Hospitals That Provide Emergency Care. Annals of Emergency Medicine, 71(3), 314-325.e1. doi:10.1016/j.annemergmed.2017.05.021Mkoka, D. A., Goicolea, I., Kiwara, A., Mwangu, M., & Hurtig, A.-K. (2014). Availability of drugs and medical supplies for emergency obstetric care: experience of health facility managers in a rural District of Tanzania. BMC Pregnancy and Childbirth, 14(1). doi:10.1186/1471-2393-14-108Beck, M. J., Okerblom, D., Kumar, A., Bandyopadhyay, S., & Scalzi, L. V. (2016). Lean intervention improves patient discharge times, improves emergency department throughput and reduces congestion. Hospital Practice, 44(5), 252-259. doi:10.1080/21548331.2016.1254559Morais Oliveira, M., Marti, C., Ramlawi, M., Sarasin, F. P., Grosgurin, O., Poletti, P.-A., … Rutschmann, O. T. (2018). Impact of a patient-flow physician coordinator on waiting times and length of stay in an emergency department: A before-after cohort study. PLOS ONE, 13(12), e0209035. doi:10.1371/journal.pone.0209035Vermeulen, M. J., Stukel, T. A., Boozary, A. S., Guttmann, A., & Schull, M. J. (2016). The Effect of Pay for Performance in the Emergency Department on Patient Waiting Times and Quality of Care in Ontario, Canada: A Difference-in-Differences Analysis. Annals of Emergency Medicine, 67(4), 496-505.e7. doi:10.1016/j.annemergmed.2015.06.028Singh, S., Lin, Y.-L., Nattinger, A. B., Kuo, Y.-F., & Goodwin, J. S. (2015). Variation in readmission rates by emergency departments and emergency department providers caring for patients after discharge. Journal of Hospital Medicine, 10(11), 705-710. doi:10.1002/jhm.2407Källberg, A.-S., Göransson, K. E., Florin, J., Östergren, J., Brixey, J. J., & Ehrenberg, A. (2015). Contributing factors to errors in Swedish emergency departments. International Emergency Nursing, 23(2), 156-161. doi:10.1016/j.ienj.2014.10.002Riga, M., Vozikis, A., Pollalis, Y., & Souliotis, K. (2015). MERIS (Medical Error Reporting Information System) as an innovative patient safety intervention: A health policy perspective. Health Policy, 119(4), 539-548. doi:10.1016/j.healthpol.2014.12.006Norman, G. R., Monteiro, S. D., Sherbino, J., Ilgen, J. S., Schmidt, H. G., & Mamede, S. (2017). The Causes of Errors in Clinical Reasoning. Academic Medicine, 92(1), 23-30. doi:10.1097/acm.0000000000001421Lisbon, D., Allin, D., Cleek, C., Roop, L., Brimacombe, M., Downes, C., & Pingleton, S. K. (2014). Improved Knowledge, Attitudes, and Behaviors After Implementation of TeamSTEPPS Training in an Academic Emergency Department. American Journal of Medical Quality, 31(1), 86-90. doi:10.1177/1062860614545123Li, L., Georgiou, A., Vecellio, E., Eigenstetter, A., Toouli, G., Wilson, R., & Westbrook, J. I. (2015). The Effect of Laboratory Testing on Emergency Department Length of Stay: A Multihospital Longitudinal Study Applying a Cross‐classified Random‐effect Modeling Approach. Academic Emergency Medicine, 22(1), 38-46. doi:10.1111/acem.12565Telem, D. A., Yang, J., Altieri, M., Patterson, W., Peoples, B., Chen, H., … Pryor, A. D. (2016). Rates and Risk Factors for Unplanned Emergency Department Utilization and Hospital Readmission Following Bariatric Surgery. Annals of Surgery, 263(5), 956-960. doi:10.1097/sla.0000000000001536Rigobello, M. C. G., Carvalho, R. E. F. L. de, Guerreiro, J. M., Motta, A. P. G., Atila, E., & Gimenes, F. R. E. (2017). The perception of the patient safety climate by professionals of the emergency department. International Emergency Nursing, 33, 1-6. doi:10.1016/j.ienj.2017.03.003Farmer, B. (2016). Patient Safety in the Emergency Department. Emergency Medicine, 48(9), 396-404. doi:10.12788/emed.2016.0052Liu, H.-C., You, J.-X., Zhen, L., & Fan, X.-J. (2014). A novel hybrid multiple criteria decision making model for material selection with target-based criteria. Materials & Design, 60, 380-390. doi:10.1016/j.matdes.2014.03.071Kou, G., Ergu, D., & Shang, J. (2014). Enhancing data consistency in decision matrix: Adapting Hadamard model to mitigate judgment contradiction. European Journal of Operational Research, 236(1), 261-271. doi:10.1016/j.ejor.2013.11.035Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2017). Supplier evaluation and selection in fuzzy environments: a review of MADM approaches. Economic Research-Ekonomska Istraživanja, 30(1), 1073-1118. doi:10.1080/1331677x.2017.1314828Barrios, M. A. O., De Felice, F., Negrete, K. P., Romero, B. A., Arenas, A. Y., & Petrillo, A. (2016). An AHP-Topsis Integrated Model for Selecting the Most Appropriate Tomography Equipment. International Journal of Information Technology & Decision Making, 15(04), 861-885. doi:10.1142/s021962201640006xYeh, D.-Y., & Cheng, C.-H. (2016). Performance Management of Taiwan’s National Hospitals. International Journal of Information Technology & Decision Making, 15(01), 187-213. doi:10.1142/s0219622014500199Chen, T.-Y. (2014). An Interactive Signed Distance Approach for Multiple Criteria Group Decision-Making Based on Simple Additive Weighting Method with Incomplete Preference Information Defined by Interval Type-2 Fuzzy Sets. International Journal of Information Technology & Decision Making, 13(05), 979-1012. doi:10.1142/s0219622014500229Gou, X., Xu, Z., & Liao, H. (2019). Hesitant Fuzzy Linguistic Possibility Degree-Based Linear Assignment Method for Multiple Criteria Decision-Making. International Journal of Information Technology & Decision Making, 18(01), 35-63. doi:10.1142/s0219622017500377Saksrisathaporn, K., Bouras, A., Reeveerakul, N., & Charles, A. (2016). Application of a Decision Model by Using an Integration of AHP and TOPSIS Approaches within Humanitarian Operation Life Cycle. International Journal of Information Technology & Decision Making, 15(04), 887-918. doi:10.1142/s0219622015500261Hsiao, B., & Chen, L.-H. (2019). Performance Evaluation for Taiwanese Hospitals by Multi-Activity Network Data Envelopment Analysis. International Journal of Information Technology & Decision Making, 18(03), 1009-1043. doi:10.1142/s0219622018500165Saaty, T. L., & Ergu, D. (2015). When is a Decision-Making Method Trustworthy? Criteria for Evaluating Multi-Criteria Decision-Making Methods. International Journal of Information Technology & Decision Making, 14(06), 1171-1187. doi:10.1142/s021962201550025xChang, K.-H., Chang, Y.-C., & Lee, Y.-T. (2014). Integrating TOPSIS and DEMATEL Methods to Rank the Risk of Failure of FMEA. International Journal of Information Technology & Decision Making, 13(06), 1229-1257. doi:10.1142/s0219622014500758Yeh, T.-M., & Huang, Y.-L. (2014). Factors in determining wind farm location: Integrating GQM, fuzzy DEMATEL, and ANP. Renewable Energy, 66, 159-169. doi:10.1016/j.renene.2013.12.003Ortíz, M. A., Felizzola, H. A., & Isaza, S. N. (2015). A contrast between DEMATEL-ANP an

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease