for estimation of injury morbidity
Bibliographic record
Abstract
Injuries are estimated to account for over 5 million deaths annually world-wide (1). In Brazil in 2011, approxi-mately 145 000 people died of injuries and 1 million were hospitalized (2). Sur-vivors of injury often experience tempo-rary or permanent disabilities, and con-sequently decreased capacity to work and quality of life (3). Injuries have a correspondingly high impact on the healthcare system—in Canada, 14 000 people died of injuries in 2004, and over 60 000 were partially disabled, generat-ing about 20 billion Canadian dollars in associated costs (4). Health information systems are cru-cial for the evaluation and monitoring of population health. In Brazil, injury deaths and hospitalizations can be tracked through the Mortality Informa-tion System (Sistema de Informação so-bre Mortalidade, SIM) and the Hospi-talization Information System (Sistema
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.041 |
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".