The Effects of the Deflection Point and Shaft Mass on Swing and Launch Parameters in the Golf Swing

This study determined whether a higher dynamic deflection point (DDP) was evident when using a driver fitted with a stiff shaft of greater mass and whether between-shaft differences were evident in swing and launch parameters. Twelve elite male golfers had three shots analysed for each of two drivers fitted with “stiff” shafts of different mass (56 g and 78 g). Six swing and launch parameters were measured by a real-time launch monitor and the (DDP) was measured using a motion analysis system. Between-shaft differences were evident for the DDP at maximum, but not at ball impact. Between-shaft differences in swing and launch parameters for the heavier shaft resulted in; lower launch angles (p\u3c0.001), increased spin rates (p\u3c0.001) and steeper attack angles (p\u3c0.001). The findings show the importance of DDP and optimising swing and launch parameters

THE EFFECTS OF THE DEFLECTION POINT AND SHAFT MASS ON SWING AND LAUNCH PARAMETERS IN THE GOLF SWING

This study determined whether a higher dynamic deflection point (DDP) was evident when using a driver fitted with a stiff shaft of greater mass and whether between-shaft differences were evident in swing and launch parameters. Twelve elite male golfers had three shots analysed for each of two drivers fitted with “stiff” shafts of different mass (56 g and 78 g). Six swing and launch parameters were measured by a real-time launch monitor and the (DDP) was measured using a motion analysis system. Between-shaft differences were evident for the DDP at maximum, but not at ball impact. Between-shaft differences in swing and launch parameters for the heavier shaft resulted in; lower launch angles (

A dynamic evaluation of how kick point location influences swing parameters and related launch conditions

In golf, many parameters of the driver can be modified to maximise hitting distance. The main objective of this study was to determine whether drivers fitted with shafts having high and low kick points would alter selected swing parameters and related launch conditions. In total, 12 elite male golfers (handicap score = 1.2 ± 1.8) had three shots analysed for two drivers fitted with \u27stiff\u27 shafts with differing kick point location. Stiffness profiles of these shafts were also measured. Five swing and related launch parameters were measured using a real-time launch monitor. The locations of the low and high kick points on each shaft during the golf swing (the dynamic kick points) were confirmed via motion analysis. The driver fitted with the shaft containing the high kick point displayed a more negative (steeper) angle of attack (p \u3c 0.01), a lower launch angle (p \u3c 0.01) and an increased spin rate (p \u3c 0.01) when compared to a driver fitted with a low kick point shaft. It is possible that the attack angle differed between drivers due to the greater amount of shaft bending found late in the downswing (80% of the downswing and just before impact). Future work is needed in this under-researched area to determine why these differences occurred

Is shape in the eye of the beholder? Assessing landmarking error in geometric morphometric analyses on live fish

Geometric morphometrics is widely used to quantify morphological variation between biological specimens, but the fundamental influence of operator bias on data reproducibility is rarely considered, particularly in studies using photographs of live animals taken under field conditions. We examined this using four independent operators that applied an identical landmarking scheme to replicate photographs of 291 live Atlantic salmon (Salmo salar L.) from two rivers. Using repeated measures tests, we found significant inter-operator differences in mean body shape, suggesting that the operators introduced a systematic error despite following the same landmarking scheme. No significant differences were detected when the landmarking process was repeated by the same operator on a random subset of photographs. Importantly, in spite of significant operator bias, small but statistically significant morphological differences between fish from the two rivers were found consistently by all operators. Pairwise tests of angles of vectors of shape change showed that these between-river differences in body shape were analogous across operator datasets, suggesting a general reproducibility of findings obtained by geometric morphometric studies. In contrast, merging landmark data when fish from each river are digitised by different operators had a significant impact on downstream analyses, highlighting an intrinsic risk of bias. Overall, we show that, even when significant inter-operator error is introduced during digitisation, following an identical landmarking scheme can identify morphological differences between populations. This study indicates that operators digitising at least a sub-set of all data groups of interest may be an effective way of mitigating inter-operator error and potentially enabling data sharing

Getting shot of elves: healing, witchcraft and fairies in the Scottish witchcraft trials

This paper re-examines the evidence of the Scottish witchcraft trials for beliefs associated by scholars with "elf-shot." Some supposed evidence for elf-shot is dismissed, but other material illuminates the interplay between illness, healing and fairy-lore in early modern Scotland, and the relationship of these beliefs to witchcraft itself

Generation and evaluation of artificial mental health records for Natural Language Processing

Funder: EPSRC Healtex Feasibility Funding (grant EP/N027280/1): "Towards Shareable Data in Clinical Natural Language Processing: Generating Synthetic Electronic Health Records"Funder: National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College LondonFunder: National Institute for Health Research Post Doctoral Fellowship award (grant number PDF-2017-10-029)Funder: Health Data Research UKFunder: Swedish Research Council(2015-00359)/the Marie Sklodowska Curie ActionsAbstract: A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data. The mental health domain is particularly challenging, partly because clinical documentation relies heavily on free text that is difficult to de-identify completely. This problem could be tackled by using artificial medical data. In this work, we present an approach to generate artificial clinical documents. We apply this approach to discharge summaries from a large mental healthcare provider and discharge summaries from an intensive care unit. We perform an extensive intrinsic evaluation where we (1) apply several measures of text preservation; (2) measure how much the model memorises training data; and (3) estimate clinical validity of the generated text based on a human evaluation task. Furthermore, we perform an extrinsic evaluation by studying the impact of using artificial text in a downstream NLP text classification task. We found that using this artificial data as training data can lead to classification results that are comparable to the original results. Additionally, using only a small amount of information from the original data to condition the generation of the artificial data is successful, which holds promise for reducing the risk of these artificial data retaining rare information from the original data. This is an important finding for our long-term goal of being able to generate artificial clinical data that can be released to the wider research community and accelerate advances in developing computational methods that use healthcare data

Generation and evaluation of artificial mental health records for Natural Language Processing

Funder: EPSRC Healtex Feasibility Funding (grant EP/N027280/1): "Towards Shareable Data in Clinical Natural Language Processing: Generating Synthetic Electronic Health Records"Funder: National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College LondonFunder: National Institute for Health Research Post Doctoral Fellowship award (grant number PDF-2017-10-029)Funder: Health Data Research UKFunder: Swedish Research Council(2015-00359)/the Marie Sklodowska Curie ActionsAbstract: A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data. The mental health domain is particularly challenging, partly because clinical documentation relies heavily on free text that is difficult to de-identify completely. This problem could be tackled by using artificial medical data. In this work, we present an approach to generate artificial clinical documents. We apply this approach to discharge summaries from a large mental healthcare provider and discharge summaries from an intensive care unit. We perform an extensive intrinsic evaluation where we (1) apply several measures of text preservation; (2) measure how much the model memorises training data; and (3) estimate clinical validity of the generated text based on a human evaluation task. Furthermore, we perform an extrinsic evaluation by studying the impact of using artificial text in a downstream NLP text classification task. We found that using this artificial data as training data can lead to classification results that are comparable to the original results. Additionally, using only a small amount of information from the original data to condition the generation of the artificial data is successful, which holds promise for reducing the risk of these artificial data retaining rare information from the original data. This is an important finding for our long-term goal of being able to generate artificial clinical data that can be released to the wider research community and accelerate advances in developing computational methods that use healthcare data

Physician supply forecast: better than peering in a crystal ball?

<p>Abstract</p> <p>Background</p> <p>Anticipating physician supply to tackle future health challenges is a crucial but complex task for policy planners. A number of forecasting tools are available, but the methods, advantages and shortcomings of such tools are not straightforward and not always well appraised. Therefore this paper had two objectives: to present a typology of existing forecasting approaches and to analyse the methodology-related issues.</p> <p>Methods</p> <p>A literature review was carried out in electronic databases Medline-Ovid, Embase and ERIC. Concrete examples of planning experiences in various countries were analysed.</p> <p>Results</p> <p>Four main forecasting approaches were identified. The supply projection approach defines the necessary inflow to maintain or to reach in the future an arbitrary predefined level of service offer. The demand-based approach estimates the quantity of health care services used by the population in the future to project physician requirements. The needs-based approach involves defining and predicting health care deficits so that they can be addressed by an adequate workforce. Benchmarking health systems with similar populations and health profiles is the last approach. These different methods can be combined to perform a gap analysis. The methodological challenges of such projections are numerous: most often static models are used and their uncertainty is not assessed; valid and comprehensive data to feed into the models are often lacking; and a rapidly evolving environment affects the likelihood of projection scenarios. As a result, the internal and external validity of the projections included in our review appeared limited.</p> <p>Conclusion</p> <p>There is no single accepted approach to forecasting physician requirements. The value of projections lies in their utility in identifying the current and emerging trends to which policy-makers need to respond. A genuine gap analysis, an effective monitoring of key parameters and comprehensive workforce planning are key elements to improving the usefulness of physician supply projections.</p

Results of Antiretroviral Treatment Interruption and Intensification in Advanced Multi-Drug Resistant HIV Infection from the OPTIMA Trial

BACKGROUND: Guidance is needed on best medical management for advanced HIV disease with multidrug resistance (MDR) and limited retreatment options. We assessed two novel antiretroviral (ARV) treatment approaches in this setting. METHODS AND FINDINGS: We conducted a 2×2 factorial randomized open label controlled trial in patients with a CD4 count≤300 cells/µl who had ARV treatment (ART) failure requiring retreatment, to two options (a) re-treatment with either standard (≤4 ARVs) or intensive (≥5 ARVs) ART and b) either treatment starting immediately or after a 12-week monitored ART interruption. Primary outcome was time to developing a first AIDS-defining event (ADE) or death from any cause. Analysis was by intention to treat. From 2001 to 2006, 368 patients were randomized. At baseline, mean age was 48 years, 2% were women, median CD4 count was 106/µl, mean viral load was 4.74 log(10) copies/ml, and 59% had a prior AIDS diagnosis. Median follow-up was 4.0 years in 1249 person-years of observation. There were no statistically significant differences in the primary composite outcome of ADE or death between re-treatment options of standard versus intensive ART (hazard ratio 1.17; CI 0.86-1.59), or between immediate retreatment initiation versus interruption before re-treatment (hazard ratio 0.93; CI 0.68-1.30), or in the rate of non-HIV associated serious adverse events between re-treatment options. CONCLUSIONS: We did not observe clinical benefit or harm assessed by the primary outcome in this largest and longest trial exploring both ART interruption and intensification in advanced MDR HIV infection with poor retreatment options. TRIAL REGISTRATION: Clinicaltrials.gov NCT00050089