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Record W2494100240 · doi:10.1002/jcu.22378

Traumatic sternal segment dislocation in a 3‐year‐old girl: Sonographic findings

2016· article· en· W2494100240 on OpenAlexaff
Nicolas Murray, Françoise Rypens, J Trudel, Marie‐Andrée Cantin, Marie‐Claude Miron

Bibliographic record

VenueJournal of Clinical Ultrasound · 2016
Typearticle
Languageen
FieldMedicine
TopicTrauma Management and Diagnosis
Canadian institutionsCégep Saint-Jean-sur-RichelieuUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineHôtel-Dieu de Québec
Fundersnot available
KeywordsMedicineGirlDislocationSurgery

Abstract

fetched live from OpenAlex

Sternal fractures are uncommon in the pediatric population, and sternal segment dislocations are even rarer with only a few cases reported in the literature. Most cases are secondary to direct trauma to the chest, but nontraumatic dislocations have been reported. The diagnosis can be difficult to establish with standard radiographs, while CT is not desirable in the pediatric population due to the associated irradiation. Ultrasound (US) can be used as the first-line modality to evaluate the sternum. We report the US findings associated with a case of traumatic sternal segment dislocation in a 3-year-old girl. © 2016 Wiley Periodicals, Inc. J Clin Ultrasound 45:45-49, 2017.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0040.003
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 head0.072
GPT teacher head0.379
Teacher spread0.308 · 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 designCase report
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

Citations6
Published2016
Admission routes1
Has abstractyes

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