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Record W4312064816 · doi:10.1055/s-0042-1755431

A Comprehensive Radiologic Review of Shoulder Girdle Trauma

2022· review· en· W4312064816 on OpenAlexaff
Muhammad Umer Nasir, Faisal Alsugair, Adnan Sheikh, Hugue A. Ouellette, Peter L. Munk, Paul I. Mallinson

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2022
Typereview
Languageen
FieldMedicine
TopicShoulder and Clavicle Injuries
Canadian institutionsUniversity of British ColumbiaVancouver General Hospital
Fundersnot available
KeywordsMedicineShoulder girdleScapulaNonunionClavicleSurgeryOsteoarthritis

Abstract

fetched live from OpenAlex

Radiologic knowledge of different fracture patterns involving the shoulder girdle is an important tool to generate clinically relevant reports, identify concomitant injuries, guide management decisions, and predict and minimize complications, such as nonunion, osteoarthritis, osteonecrosis, and hardware failure. Complex unstable injuries like scapulothoracic dissociation can also occur because of shoulder girdle trauma. Management options may vary from conservative to surgical, depending on the fracture type and patient factors. Injuries around the shoulder girdle can involve the glenohumeral articulation, scapula, superior shoulder suspensory complex, acromioclavicular joints, and scapulothoracic articulation.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.008
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.051
GPT teacher head0.429
Teacher spread0.378 · 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 designNot applicable
Domainnot available
GenreReview

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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