Research Tracking Patterns of Enteric Illnesses in Populations and Communities
Bibliographic record
Abstract
BACKGROUND: Enteric illness arising from contaminated water and food is a major health concern worldwide, and tracking the incidences and severity of outbreaks is still a challenging task. Most developed and developing countries have administrative databases for medical visits and services maintained by the government and/or health insurance authorities. Although these databases could be extremely valuable resources to track patterns of environmental and other health issues, test hypotheses, and develop epidemiologic models and predictions, very little research has been done to develop methods to ensure the robustness of such databases and to demonstrate their utility as a research tool. OBJECTIVES: We used the Medical Services Plan (MSP) database of British Columbia, Canada, to develop innovative ways to use medical billing and fee-for-services data to track long-term patterns of enteric illness at the level of populations and communities. RESULTS: To illustrate the power and robustness of the method, we provided several examples covering 8 years of data from each of four communities covering a large range of population size. Not only could this method generalize to other diseases for which specific fee item markers can be found, but also it gives results consistent with a known outbreak and yields data patterns, which could not be revealed by the currently used methods. Because diagnostic code and fee item data for medical services are collected by most medical insurance agencies, our method can have global applications for tracking enteric and other illnesses at the level of populations and communities.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".