Níveis de alcoolemia e mortalidade por acidentes de trânsito na cidade do Rio de Janeiro
Bibliographic record
Abstract
Trata-se de estudo epidemiológico descritivo. Objetivou descrever o perfil das vítimas fatais por acidentes de trânsito na cidade do Rio de Janeiro a partir dos registros do Instituto Medico Legal e compará-los aos níveis de alcoolemia detectados através do exame laboratorial. A coleta de dados obedeceu à Resolução 196/96 do Conselho Nacional de Saúde. Os dados foram levantados no arquivo do IML, através dos registros nos prontuários de vítimas fatais por acidentes de trânsito, do universo das vítimas por todas as causas externas, e registrados a partir de um sistema de informação específico, compilados e tabulados pelo programa estatístico EPI INFO, no período compreendido entre janeiro e fevereiro de 2005. Evidenciou-se que 27,8% das vítimas fatais apresentaram alcoolemia detectada. Em 64% desses, o nível de alcoolemia foi acima de 0,6 g/L, enquanto 36% apresentaram um percentual significativo de mortalidade com níveis abaixo do limite legal estabelecido no Brasil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".