Measuring the Masses: The Current State of Mass-Gathering Medical Case Reporting (Paper 1)
Bibliographic record
Abstract
INTRODUCTION: Case reports are commonly used to report the health outcomes of mass gatherings (MGs), and many published reports of MGs demonstrate substantial heterogeneity of included descriptors. As such, it is challenging to perform rigorous comparisons of health services and outcomes between similar and dissimilar events. The degree of variation in published reports has not yet been investigated. OBJECTIVE: Examine patterns of post-event medical reporting in the existing literature and identify inconsistencies in reporting. METHODS: A systematic review of case reports was conducted. Included were English studies, published between January 2009 and December 2018, in Prehospital and Disaster Medicine (PDM) or Current Sports Medicine Reports (CSMR). Analysis of each paper was used to develop a list of 27 categories of data. RESULTS: Seventy-five studies were initially reviewed with 54 publications meeting the inclusion criteria. Forty-two were full case reports (78%) and 12 were conference proceedings (22%). Of the 27 categories of data studied, only 13 were consistently reported in more than 50% of publications. Reporting patterns included inconsistent use of terminology/language and variable retrievability of reports. Reporting on event descriptors, hazard and risk analysis, and clinical outcomes were also inconsistent. DISCUSSION: Case reports are essential tools for researchers and event team members such as medical directors and event producers. The authors found that current case reports, in addition to being inconsistent in content, were generally descriptive rather than explanatory; that is, focused on describing the outcomes as opposed to exploring possible connections between context and health outcomes. CONCLUSION: This paper quantifies and demonstrates the current state of heterogeneity in MG event reporting. This heterogeneity is a significant impediment to the functional use of published reports to further the science of MG planning and to improve health outcomes. Future work based on the insights gained from this analysis will aim to align and standardize reporting to improve the quality and value of event reporting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.314 | 0.627 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.029 | 0.032 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".