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Record W4310059409 · doi:10.1016/j.xrrt.2022.10.007

Anteromedial coronoid fractures: technical description of an extensile surgical approach and outcomes from a small series using this technique

2022· article· en· W4310059409 on OpenAlexaff
Mikaela J. Peters, Oren Zarnett, Zafeiria Glaris, Adrian Huang, Jeffrey M. Pike, Parham Daneshvar, Thomas J. Goetz

Bibliographic record

VenueJSES Reviews Reports and Techniques · 2022
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsKingston Health Sciences CentreSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsElbowMedicineLigamentFixation (population genetics)Coronoid processDissection (medical)SurgeryAnatomy

Abstract

fetched live from OpenAlex

Background: Varus posteromedial rotatory instability is a difficult clinical problem to diagnose and treat. Fixation of the anteromedial coronoid fracture is often necessary to achieve elbow stability. We describe an extensile surgical approach to the anteromedial coronoid. Methods: A retrospective review was performed of all patients at our institution who had anteromedial coronoid fracture fixed with this approach between 2012 and 2020. Results: Six patients were identified. They all achieved a stable elbow. Four of 6 developed heterotopic ossification and 2/6 required further surgery for this. Only 1 patient had a transient ulnar sensory loss. Conclusion: We describe an approach to the coronoid that allows great visualization of the joint and access to large coronoid fractures. The approach is extensile and does not require extensive dissection or work around the ulnar nerve. Access to fracture and for fixation can be improved by release of the common flexor pronator origin and the medial collateral ligament.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.061
GPT teacher head0.319
Teacher spread0.257 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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