Using Participant Event Monitoring in a Cohort Study of Unintentional Injuries Among Children and Adolescents
Bibliographic record
Abstract
OBJECTIVES: We conducted a 3-year cohort study of 407 youths aged 9 to 18 years to develop multivariable risk prediction models of agriculture-related injuries. METHODS: Data were obtained via participant event monitoring, with youths self-reporting injuries and exposures in daily diaries over a 13-week period. We evaluated data quality by comparing injury self-reports with other injury data. RESULTS: Semilogarithmic plots of rates of all unintentional injuries combined (US data from 2000) as well as of agriculture-related injuries (US and Canadian data from 19 previous studies) graphed as a function of injury severity exhibited linearity, as did plots based on the present results. Severity-specific unintentional injury rates were 1.4- to 4.3-times higher than national rates, suggesting that our methodology can significantly reduce injury underreporting. In addition, at each severity level, estimated agriculture-related injury rates were 5.8- to 9.3-times higher than rates from previous national, regional, and state-based studies. CONCLUSIONS: Our approach to participant event monitoring can be implemented with youths aged 9 to 18 years and will yield reliable daily data on unintentional injuries.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".