Enhancing the Minimum Data Set for Mass-Gathering Research and Evaluation: An Integrative Literature Review
Bibliographic record
Abstract
INTRODUCTION: In 2012, a minimum data set (MDS) was proposed to enable the standardized collection of biomedical data across various mass gatherings. However, the existing 2012 MDS could be enhanced to allow for its uptake and usability in the international context. The 2012 MDS is arguably Australian-centric and not substantially informed by the literature. As such, an MDS with contributions from the literature and application in the international settings is required. METHODS: This research used an integrative literature review design. Manuscripts were collected using keyword searches from databases and journal content pages from 2003 through 2013. Data were analyzed and categorized using the existing 2012 MDS as a framework. RESULTS: In total, 19 manuscripts were identified that met the inclusion criteria. Variation in the patient presentation types was described in the literature from the mass-gathering papers reviewed. Patient presentation types identified in the literature review were compared to the 2012 MDS. As a result, 16 high-level patient presentation types were identified that were not included in the 2012 MDS. CONCLUSION: Adding patient presentation types to the 2012 MDS ensures that the collection of biomedical data for mass-gathering health research and evaluation remains contemporary and comprehensive. This review proposes the addition of 16 high-level patient presentation categories to the 2012 MDS in the following broad areas: gastrointestinal, obstetrics and gynecology, minor illness, mental health, and patient outcomes. Additionally, a section for self-treatment has been added, which was previously not included in the 2012 MDS, but was widely reported in the literature.
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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 | 0.160 | 0.322 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.051 | 0.033 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".