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Record W2073473934 · doi:10.2147/oajsm.s7980

Rotator cuff troublemakers: pitfalls of MRI and ultrasound

2009· article· en· W2073473934 on OpenAlexaff
Bruce Forster, Chingkoe, Mark White, Louis Louis, Andrews

Bibliographic record

VenueOpen Access Journal of Sports Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRotator cuffMedicineUltrasoundMagnetic resonance imagingRotator cuff injuryRadiologyMedical physics

Abstract

fetched live from OpenAlex

Rotator cuff pathology is routinely evaluated in many imaging centers with both magnetic resonance imaging (MRI) and ultrasound. Despite good diagnostic accuracy using each of these modalities, certain limitations persist. In this pictorial essay, we describe five potential "troublemakers" of rotator cuff pathology which are recurrent themes in our busy shoulder referral center. The comparison of imaging findings on MRI and ultrasound are discussed. An awareness of these potential pitfalls will help improve radiologists' diagnostic accuracy of rotator cuff pathology, and allow the clinician to optimize imaging referral and better interpret the subsequent report.

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)0.013
metaresearch head score (Gemma)0.068
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: none
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.068
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0020.008
Scholarly communication0.0030.006
Open science0.0020.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0010.001

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 head0.047
GPT teacher head0.407
Teacher spread0.360 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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