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Participação da radiologia nas perícias necroscópicas de baleados realizadas no Instituto Médico-Legal do Rio de Janeiro

2005· article· pt· W1975891797 on OpenAlexaff
Hilton Augusto Koch, Casimiro Abreu Possante de Almeida, Bianca Gutfilen

Bibliographic record

VenueRadiologia Brasileira · 2005
Typearticle
Languagept
FieldMedicine
TopicAutopsy Techniques and Outcomes
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesMedicineGeographyPolitical scienceGynecologyArt

Abstract

fetched live from OpenAlex

OBJETIVO: Este trabalho aborda as conseqüências de laudos necroscópicos incompletos de baleados, nos casos em que não foi possível o uso de recursos radiológicos para localizar os projéteis de arma de fogo. MATERIAIS E MÉTODOS: Foram analisados 8.185 laudos necroscópicos do Instituto Médico-Legal Afrânio Peixoto, Rio de Janeiro, RJ, referentes à demanda total de cadáveres no período de 1º de janeiro a 31 de dezembro de 2001, dos quais 3.122 casos corresponderam a necropsias de baleados. RESULTADOS: Desses casos, 309 corpos foram sepultados contendo ainda, no seu interior, projéteis de arma de fogo, podendo suscitar futuras indagações judiciais. No mesmo período foram solicitadas 23 exumações, 12 delas com a finalidade de recolher projéteis. Foram calculados os gastos relacionados à realização de necropsias de baleados - R$ 996,85 - e custos alusivos à realização de exumações com a finalidade de recolher projéteis de arma de fogo - R$ 1.155,40, visando a estabelecer o montante financeiro que poderia ser poupado pelos cofres públicos, a ser alocado para finalidades outras, se a perícia médico-legal de baleados, no exame inicial, obtivesse sucesso. CONCLUSÃO: Os resultados permitiram concluir que todos os atos necroscópicos de baleados devem seguir protocolos específicos, uma vez que perícias incompletas exigem exumação posterior, com gastos adicionais desnecessários.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.334
Teacher spread0.294 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2005
Admission routes1
Has abstractyes

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