Measuring the Masses: A Proposed Template for Post-Event Medical Reporting (Paper 4)
Bibliographic record
Abstract
BACKGROUND: Standardizing and systematizing the reporting of health outcomes from mass gatherings (MGs) will improve the quality of data being reported. Setting minimum standards for case reporting is an important strategy for improving data quality. This paper is one of a series of papers focused on understanding the current state, and shaping the future state, of post-event case reporting. METHODS: Multiple data sources were used in creating a lean, yet comprehensive list of essential reporting fields, including a: (1) literature synthesis drawn from analysis of 54 post-event case reports; (2) comparison of existing data models for MGs; (3) qualitative analysis of gaps in current case reports; and (4) set of data domains developed based on the preceding sources. FINDINGS: Existing literature fails to consistently report variables that may be essential for not only describing the health outcomes of a given event, but also for explaining those outcomes. In the context of current and future state reporting, 25 essential variables were identified. The essential variables were organized according to four domains, including: (i) Event Domain; (ii) Hazard and Risk Domain; (iii) Capacity Domain; and (iv) Clinical Domain. DISCUSSION: The authors propose a first-generation template for post-event medical reporting. This template standardizes the reporting of 25 essential variables. An accompanying data dictionary provides background and standardization for each of the essential variables. Of note, this template is lean and will develop over time, with input from the international MG community. In the future, additional groups of variables may be helpful as "overlays," depending on the event category and type. CONCLUSIONS: This paper presents a template for post-event medical reporting. It is hoped that consistent reporting of essential variables will improve both data collection and the ability to make comparisons between events so that the science underpinning MG health can continue to advance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".