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Níveis de alcoolemia e mortalidade por acidentes de trânsito na cidade do Rio de Janeiro

2007· article· pt· W2155990160 on OpenAlexaff
Ângela Maria Mendes Abreu, José Mauro Bráz de Lima, Lidiane Mendes da Silva

Bibliographic record

VenueEscola Anna Nery · 2007
Typearticle
Languagept
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesMedicineGeographyArt

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.061
GPT teacher head0.452
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2007
Admission routes1
Has abstractyes

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