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Record W4220786556 · doi:10.1016/j.xrrt.2022.01.009

Median nerve injury after Latarjet open surgery: a case report

2022· article· en· W4220786556 on OpenAlexaff
Catherine Fleury, Guillaume Gagnon, Sonia Bédard, François Vézina

Bibliographic record

VenueJSES Reviews Reports and Techniques · 2022
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsLatarjet procedureMedicineScopusAnterior shoulderElbowWeb of scienceSurgeryMEDLINEMeta-analysisInternal medicine

Abstract

fetched live from OpenAlex

The Bristow-Latarjet procedure is gaining popularity worldwide as the first-choice treatment for glenoid bone loss–associated recurrent shoulder instability, showing excellent results for, among others, contact sport athletes.1,5,7,9 Nonetheless, overall complication rates remain considerably high after a Latarjet procedure, reaching 15% in the latest report.6 Of these, neurologic complications affect one to two percent of patients. These neurologic lesions, such as axillary or musculocutaneous nerve injuries, are well described.

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)0.004
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.004
Science and technology studies0.0040.002
Scholarly communication0.0030.004
Open science0.0020.003
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.053
GPT teacher head0.374
Teacher spread0.322 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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