Measuring the Masses: Guidelines for Publication of Case Reports on Mass Gatherings
Bibliographic record
Abstract
Introduction: The science supporting event medicine is growing rapidly. In order to improve the ability of researchers to access event data and improve the quality of publishing mass gathering cases, it would be of benefit to standardize event reports to permit the comparison of similar events across local and national boundaries. These data would support the development of practice standards across settings. Aim: The authors propose the creation of a publication guideline to support authors seeking to publish in this field. Method: Derivation study via analysis of published case reports using the Delphi process. Results: Data elements were inconsistently reported within published case reports. Categories of variables included: event demographics (descriptors of date, time, genre, activity, risks), attendance and population demographics, data related to climate and weather conditions, composition and deployment of an onsite medical team, highest level of care available onsite, patient demographics, patient presentations and measures of impact on the local health care system such as transfer to hospital rates. Of note, there was a high incidence of “missing” variables that would be of central interest to researchers. Discussion: Approaches to standardizing the collection and reporting of data are often discussed in the health care literature. The benefits of consistent, structured data collection are well understood. In the context of mass gathering event case reporting, the time is ripe for the introduction of a guideline (with accompanying guidance notes and dictionary). The proposed guideline requires the input of subject matter experts (in progress) to enhances its relevance and uptake. This work is timely as there is ongoing work on improving an international event medicine registry. If the evolution of both proceeds in lockstep, there is a good chance that access to a rigorous data set will become a reality.
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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 | 0.405 | 0.616 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.048 | 0.026 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.018 | 0.016 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.033 |
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".