Mass-Gathering Medicine: Creation of an Online Event and Patient Registry
Bibliographic record
Abstract
UNLABELLED: INTRODUCTION/PROBLEM: A review of the mass-gathering medicine literature confirms that the research community currently lacks a standardized approach to data collection and reporting in relation to large-scale community events. This lack of consistency, particularly with regard to event characteristics, patient characteristics, acuity determination, and reporting of illness and injury rates makes comparisons between and across events difficult. In addition, a lack of access to good data across events makes planning medical support on-site, for transport, and at receiving hospitals, challenging. This report describes the development of an Internet-hosted, secure registry for event and patient data in relation to mass gatherings. METHODS: Descriptive; development and pilot testing of a Web-based event and patient registry. RESULTS: Several iterations of the registry have resulted in a cross-event platform for standardized data collection at a variety of events. Registry and reporting field descriptions, successes, and challenges are discussed based on pilot testing and early implementation over two years of event enrollment. CONCLUSION: The Mass-Gathering Medicine Event and Patient Registry provides an effective tool for recording and reporting both event and patient-related variables in the context of mass-gathering events. Standardizing data collection will serve researchers and policy makers well. The structure of the database permits numerous queries to be written to generate standardized reports of similar and dissimilar events, which supports hypothesis generation and the development of theoretical foundations in mass-gathering medicine.
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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 | 0.033 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".