Additional file 1 of Examining the relationship between food insecurity and causes of injury in Canadian adults and adolescents
Bibliographic record
Abstract
Additional file 1: Table S1. ICD-10-CA code for injury-related ED visits. Table S2. Food insecurity status, based on CCHS 18-item questionnaire. Table S3. Rate per 10,000 persons of past-year injury-related ED visit by food insecurity status, stratified by sex and age. Table S4. Incidence rate ratio from adjusted Poisson model on past-year injury-related ED visits in overall sample. Table S5. Incidence rate ratio from Poisson models on past-year injury-related ED visits in overall sample and by sex and age subsamples. Table S6. Sensitivity test on all-cause injury-related ED visits. Table S7. Poisson models on past-year ED visits due to cause-specific injuries in overall sample . Fig. S1. Adjusted predicted probability of injury by food insecurity status: overall sample and by sex and age subsamples. Fig. S2. Adjusted predicted probability of specific non-intentional injury by food insecurity status: overall sample.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.571 | 0.032 |
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".