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Record W2797709319 · doi:10.1093/pch/pxx183

L’évaluation médicale des fractures en cas de soupçons de maltraitance : les nourrissons et les jeunes enfants atteints d’une lésion squelettique

2018· article· fr· W2797709319 on OpenAlexaff
Laurel Chauvin‐Kimoff, Claire Allard‐Dansereau, Margaret Colbourne

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languagefr
FieldMedicine
TopicChild Abuse and Related Trauma
Canadian institutionsCanadian Paediatric Society
Fundersnot available
KeywordsHumanitiesMedicineArt

Abstract

fetched live from OpenAlex

Les fractures sont des lésions courantes pendant l’enfance. La plupart sont causées par un traumatisme accidentel, mais les traumatismes non accidentels (maltraitance) sont de graves causes de fractures qui ne sont pas toujours dépistées, notamment chez les nourrissons et les jeunes enfants. Le présent point de pratique passe en revue les caractéristiques cliniques qui soulèvent des soupçons de lésions squelettiques non accidentelles et expose une méthode de prise en charge reposant sur les publications à jour et les lignes directrices publiées. Il souligne que les cliniciens sont tenus de signaler les soupçons de maltraitance aux services de protection de l’enfance. Il ne traite pas des fractures crâniennes isolées.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.023
GPT teacher head0.340
Teacher spread0.317 · 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 teacher head, not a consensus.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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