Measurement and Explanation of the Intensity of Co Co-publication in Scientific Research: An Analysis at the Laboratory Level

In this paper we study networks of academic researchers on an aggregated laboratory scale. We propose a measurement of the intensity of cooperation between laboratories, and attempt to account for its intra- and inter-town variations in relation to a number of characteristics: geographic distance between laboratories, specialization of laboratories, size of their scientific community, productivity, quality of their publications and international openness. Cooperative relations are identified on the basis of data on co-publication. These data concern French physicists from the Centre National de la Recherche Scientifique (CNRS), between 1992 and 1997.

Measurement and Explanation of the Intensity of Co-publication in Scientific Research: An Analysis at the Laboratory Level

In order to study networks of collaboration between researchers, we propose a simple measure of the intensity of collaboration, which can be easily interpreted in terms of relative probability and aggregated at the laboratory level. We first use this measure to characterize the relations of collaboration, as defined in terms of co-publication between the scientists of the French “Centre National de la Recherche Scientifique” (CNRS) in the field of condensed-matter physic, during the six-year period 1992-1997. We then use it to investigate the importance of various factors of collaboration: mainly the geographical distance between laboratories, but also their specialization and size, their productivity and the quality of their publications, and their international openness. We find that the average intensity of co-publication of researchers within laboratories is about 40 times higher than the average intensity between laboratories if they are located in the same towns, and that it is 100 times higher than the intensity between laboratories which are not located in the same towns. Yet, geographical distance does not have a significant impact, or a very weak one, on the existence and intensity of co-publication between laboratories located in different towns. What matters is immediate proximity. We also find that the productivity of laboratories, their size and specialization profiles are significant determinants of collaboration.

Identifying Age, Cohort and Period Effects in Scientific Research Productivity: Discussion and Illustration Using Simulated and Actual Data on French Physicists

The identification of age, cohort (vintage), and period (year) effects in a panel of individuals or other units is an old problem in the social sciences, but one that has not been much studied in the context of measuring researcher productivity. In the context of a semi-parametric model of productivity where these effects are assumed to enter in an additive manner, we present the conditions necessary to identify and test for the presence of the three effects. In particular we show that failure to specify precisely the conditions under which such a model is identified can lead to misleading conclusions about the productivity-age relationship. We illustrate our methods using data on the publications 1986-1997 by 465 French condensed matter physicists who were born between 1936 and 1960.

Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

Background The primary objective of this review is to assess the accuracy of machine learning methods in their application of triaging the acuity of patients presenting in the Emergency Care System (ECS). The population are patients that have contacted the ambulance service or turned up at the Emergency Department. The index test is a machine-learning algorithm that aims to stratify the acuity of incoming patients at initial triage. This is in comparison to either an existing decision support tool, clinical opinion or in the absence of these, no comparator. The outcome of this review is the calibration, discrimination and classification statistics. Methods Only derivation studies (with or without internal validation) were included. MEDLINE, CINAHL, PubMed and the grey literature were searched on the 14th December 2019. Risk of bias was assessed using the PROBAST tool and data was extracted using the CHARMS checklist. Discrimination (C-statistic) was a commonly reported model performance measure and therefore these statistics were represented as a range within each machine learning method. The majority of studies had poorly reported outcomes and thus a narrative synthesis of results was performed. Results There was a total of 92 models (from 25 studies) included in the review. There were two main triage outcomes: hospitalisation (56 models), and critical care need (25 models). For hospitalisation, neural networks and tree-based methods both had a median C-statistic of 0.81 (IQR 0.80-0.84, 0.79-0.82). Logistic regression had a median C-statistic of 0.80 (0.74-0.83). For critical care need, neural networks had a median C-statistic of 0.89 (0.86-0.91), tree based 0.85 (0.84-0.88), and logistic regression 0.83 (0.79-0.84). Conclusions Machine-learning methods appear accurate in triaging undifferentiated patients entering the Emergency Care System. There was no clear benefit of using one technique over another; however, models derived by logistic regression were more transparent in reporting model performance. Future studies should adhere to reporting guidelines and use these at the protocol design stage. Registration and funding This systematic review is registered on the International prospective register of systematic reviews (PROSPERO) and can be accessed online at the following URL: https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020168696 This study was funded by the NIHR as part of a Clinical Doctoral Research Fellowship

The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study : the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene—a protocol

Background Demand for both the ambulance service and the emergency department (ED) is rising every year and when this demand is excessive in both systems, ambulance crews queue at the ED waiting to hand patients over. Some transported ambulance patients are ‘low-acuity’ and do not require the treatment of the ED. However, paramedics can find it challenging to identify these patients accurately. Decision support tools have been developed using expert opinion to help identify these low acuity patients but have failed to show a benefit beyond regular decision-making. Predictive algorithms may be able to build accurate models, which can be used in the field to support the decision not to take a low-acuity patient to an ED. Methods and analysis All patients in Yorkshire who were transported to the ED by ambulance between July 2019 and February 2020 will be included. Ambulance electronic patient care record (ePCR) clinical data will be used as candidate predictors for the model. These will then be linked to the corresponding ED record, which holds the outcome of a ‘non-urgent attendance’. The estimated sample size is 52,958, with 4767 events and an EPP of 7.48. An XGBoost algorithm will be used for model development. Initially, a model will be derived using all the data and the apparent performance will be assessed. Then internal-external validation will use non-random nested cross-validation (CV) with test sets held out for each ED (spatial validation). After all models are created, a random-effects meta-analysis will be undertaken. This will pool performance measures such as goodness of fit, discrimination and calibration. It will also generate a prediction interval and measure heterogeneity between clusters. The performance of the full model will be updated with the pooled results. Discussion Creating a risk prediction model in this area will lead to further development of a clinical decision support tool that ensures every ambulance patient can get to the right place of care, first time. If this study is successful, it could help paramedics evaluate the benefit of transporting a patient to the ED before they leave the scene. It could also reduce congestion in the urgent and emergency care system. Trial Registration This study was retrospectively registered with the ISRCTN: 1212128

Why do ambulance services have different non-transport rates? A national cross sectional study

BACKGROUND: Some patients calling ambulance services (known as Emergency Medical Services internationally) are not transported to hospital. In England, national ambulance quality indicators show considerable variation in non-transport rates between the ten large regional ambulance services. The aim of this study was to explain variation between ambulance services in two types of non-transport: discharge at scene and telephone advice. METHODS: Mixed model logistic regressions using one month of data (November 2014) from the Computer Aided Despatch systems of the ten large regional ambulance services in England. RESULTS: 41% (251 677/615 815) of patients calling ambulance services were not transported to hospital. Most were discharged at scene after attendance by an ambulance (29% n = 182 479) and a small percentage were given telephone advice (7% n = 40 679). Discharge at scene rates varied by patient-level factors e.g. they were higher for elderly patients, where the reason for calling was falls, and for patients attended by paramedics with extended skills. These patient-level factors did not explain variation between ambulance services. After adjustment for patient-level factors, the following ambulance service level factors explained variation in discharge at scene rates: proportion of patients attended by paramedics with extended skills (odds ratio 1.05 (95% CI 1.04, 1.07)), the perception of ambulance service staff that paramedics with extended skills were established and valued within the workforce (odds ratio 1.84 (1.45, 2.33), and the perception of ambulance service staff that senior management viewed non-transport as risky (odds ratio 0.78 (0.63, 0.98)). Variation in telephone advice rates could not be explained. CONCLUSIONS: Variation in discharge at scene rates was explained by differences in workforce configuration and managerial motivation, factors that are largely modifiable by ambulance services

The Safety INdEx of prehospital on scene triage (SINEPOST) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene

One of the main problems currently facing the delivery of safe and effective emergency care is excess demand, which causes congestion at different time points in a patient’s journey. The modern case-mix of prehospital patients is broad and complex, diverging from the traditional ‘time critical accident and emergency’ patients. It now includes many low-acuity patients and those with social care and mental health needs. In the ambulance service, transport decisions are the hardest to make and paramedics decide to take more patients to the ED than would have a clinical benefit. As such, this study asked the following research questions: In adult patients attending the ED by ambulance, can prehospital information predict an avoidable attendance? What is the simulated transportability of the model derived from the primary outcome? A linked dataset of 101,522 ambulance service and ED ambulance incidents linked to their respective ED care record from the whole of Yorkshire between 1st July 2019 and 29th February 2020 was used as the sample for this study. A machine learning method known as XGBoost was applied to the data in a novel way called Internal-External Cross Validation (IECV) to build the model. The results showed great discrimination with a C-statistic of 0.81 (95%CI 0.79–0.83) and excellent calibration with an O:E ratio was 0.995 (95% CI 0.97–1.03), with the most important variables being a patient’s mobility, their physiological observations and clinical impression with psychiatric problems, allergic reactions, cardiac chest pain, head injury, non-traumatic back pain, and minor cuts and bruising being the most important. This study has successfully developed a decision-support model that can be transformed into a tool that could help paramedics make better transport decisions on scene, known as the SINEPOST model. It is accurate, and spatially validated across multiple geographies including rural, urban, and coastal. It is a fair algorithm that does not discriminate new patients based on their age, gender, ethnicity, or decile of deprivation. It can be embedded into an electronic Patient Care Record system and automatically calculate the probability that a patient will have an avoidable attendance at the ED, if they were transported. This manuscript complies with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement (Moons KGM, 2015)

Understanding variation in ambulance service non-conveyance rates: a mixed methods study

Background In England in 2015/16, ambulance services responded to nearly 11 million calls. Ambulance Quality Indicators show that half of the patients receiving a response by telephone or face to face were not conveyed to an emergency department. A total of 11% of patients received telephone advice only. A total of 38% of patients were sent an ambulance but were not conveyed to an emergency department. For the 10 large ambulance services in England, rates of calls ending in telephone advice varied between 5% and 17%. Rates of patients who were sent an ambulance but not conveyed to an emergency department varied between 23% and 51%. Overall non-conveyance rates varied between 40% and 68%. Objective To explain variation in non-conveyance rates between ambulance services. Design A sequential mixed methods study with five work packages. Setting Ten of the 11 ambulance services serving > 99% of the population of England. Methods (1) A qualitative interview study of managers and paramedics from each ambulance service, as well as ambulance commissioners (totalling 49 interviews undertaken in 2015). (2) An analysis of 1 month of routine data from each ambulance service (November 2014). (3) A qualitative study in three ambulance services with different published rates of calls ending in telephone advice (120 hours of observation and 20 interviews undertaken in 2016). (4) An analysis of routine data from one ambulance service linked to emergency department attendance, hospital admission and mortality data (6 months of 2013). (5) A substudy of non-conveyance for people calling 999 with breathing problems. Results Interviewees in the qualitative study identified factors that they perceived to affect non-conveyance rates. Where possible, these perceptions were tested using routine data. Some variation in non-conveyance rates between ambulance services was likely to be due to differences in the way rates were calculated by individual services, particularly in relation to telephone advice. Rates for the number of patients sent an ambulance but not conveyed to an emergency department were associated with patient-level factors: age, sex, deprivation, time of call, reason for call, urgency level and skill level of attending crew. However, variation between ambulance services remained after adjustment for patient-level factors. Variation was explained by ambulance service-level factors after adjustment for patient-level factors: the percentage of calls attended by advanced paramedics [odds ratio 1.05, 95% confidence interval (CI) 1.04 to 1.07], the perception of ambulance service staff and commissioners that advanced paramedics were established and valued within the workforce of an ambulance service (odds ratio 1.84, 95% CI 1.45 to 2.33), and the perception of ambulance service staff and commissioners that senior management was risk averse regarding non-conveyance within an ambulance service (odds ratio 0.78, 95% CI 0.63 to 0.98). Limitations Routine data from ambulance services are complex and not consistently collected or analysed by ambulance services, thus limiting the utility of comparative analyses. Conclusions Variation in non-conveyance rates between ambulance services in England could be reduced by addressing variation in the types of paramedics attending calls, variation in how advanced paramedics are used and variation in perceptions of the risk associated with non-conveyance within ambulance service management. Linking routine ambulance data with emergency department attendance, hospital admission and mortality data for all ambulance services in the UK would allow comparison of the safety and appropriateness of their different non-conveyance rates. Funding The National Institute for Health Research Health Services and Delivery Research programme

Melanocortin 1 Receptor Variants in an Irish Population

The identification of an association between variants in the human melanocortin 1 receptor (MC1R) gene and red hair and fair skin, as well as the relation between variants of this gene and coat color in animals, suggests that the MC1R is an integral control point in the normal pigmentation phenotype. In order to further define the contribution of MC1R variants to pigmentation in a normal population, we have looked for alterations in this gene in series of individuals from a general Irish population, in whom there is a preponderance of individuals with fair skin type. Seventy-five per cent contained a variant in the MC1R gene, with 30% containing two variants. The Arg151Cys, Arg160Trp, and Asp294His variants were significantly associated with red hair (p = 0.0015, p < 0.001, and p < 0.005, respectively). Importantly, no individuals harboring two of these three variants did not have red hair, although some red-haired individuals only showed one alteration. The same three variants were also over-represented in individuals with light skin type as assessed using a modified Fitzpatrick scale. Despite these associations many subjects with dark hair/darker skin type harbored MC1R variants, but there was no evidence of any particular association of variants with the darker phenotype. The Asp294His variant was similarly associated with red hair in a Dutch population, but was infrequent in red-headed subjects from Sweden. The Asp294His variant was also significantly associated with nonmelanoma skin cancer in a U.K. population. The results show that the Arg151Cys, Arg160Trp, and Asp294His variants are of key significance in determining the pigmentary phenotype and response to ultraviolet radiation, and suggest that in many cases the red-haired component and in some cases fair skin type are inherited as a Mendelian recessive