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Indicações de tomografia de crânio em crianças com trauma cranioencefálico leve

2013· article· pt· W2989552930 on OpenAlexaff
Enrico Ghizoni, A Fraga, Emílio Carlos Elias Baracat, Andrei Fernandes Joaquim, Gustavo Pereira Fraga, Sandro Rizoli, Barto Nascimento

Bibliographic record

VenueRevista do Colégio Brasileiro de Cirurgiões · 2013
Typearticle
Languagept
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNuclear medicineMedicine

Abstract

fetched live from OpenAlex

A reunião de revista "Telemedicina Baseada em Evidência - Cirurgia do Trauma e Emergência" (TBE-CiTE) realizou uma revisão crítica da literatura e selecionou os três artigos mais relevantes e atuais sobre a indicação de tomografia de crânio em pacientes pediátricos com trauma craniencefálico leve (TCE). O primeiro trabalho identificou pacientes vítimas de TCE leve com fatores de alto e baixo risco de apresentarem lesões intracranianas vistas à tomografia computadorizada (TC) de crânio e com necessidade de intervenção neurocirúrgica. O segundo trabalho avaliou o uso das recomendações do "National Institute of Clinical Excellence" em pacientes pediátricos com TCE, e utilizou como variáveis de desfecho a realização de TC ou internação hospitalar. O último artigo analisou e identificou os pacientes onde a TC de crânio seria desnecessária e, portanto, não deve ser feita rotineiramente. Baseado nessa revisão crítica da literatura e a discussão com especialistas, o TBE-CiTE concluiu que é importante evitar a exposição desnecessária de crianças com TCE leve à radiação ionizante da TC de crânio. O grupo favoreceu a utilização do guideline do PECARN onde ECG de 14, alteração do nível de consciência ou fratura do crânio palpável são indicações de TC de crânio, ou quando a experiência do médico, achados múltiplos ou piora dos sintomas ocorrerem.

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)0.006
metaresearch head score (Gemma)0.042
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.006
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.024
GPT teacher head0.278
Teacher spread0.254 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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