MétaCan
Menu
Back to cohort
Record W4318465311 · doi:10.1177/08465371231152151

Recommendations for the Management of Incidental Musculoskeletal Findings on MRI and CT

2023· article· en· W4318465311 on OpenAlexaffabout
Gina Di Primio, Gordon Boyd, Christopher Fung, Casey Hurrell, Gary Brahm, Jeffery R. Bird, Steven J. Co, Iain D. C. Kirkpatrick

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsUniversity of ManitobaUniversity of British ColumbiaWestern UniversityUniversity of SaskatchewanUniversity of AlbertaDalhousie UniversityMcMaster UniversityOakville-Trafalgar Memorial Hospital
Fundersnot available
KeywordsMedicineRadiologyMagnetic resonance imagingPubic symphysisMedical physicsPelvis

Abstract

fetched live from OpenAlex

The Canadian Association of Radiologists (CAR) Incidental Findings Working Group consists of both academic subspeciality and general radiologists tasked with either adapting American College of Radiology (ACR) guidelines to meet the needs of Canadian radiologists or authoring new guidelines where appropriate. In this case, entirely new guidelines to deal with incidental musculoskeletal findings that may be encountered on thoracoabdominal computed tomography or magnetic resonance imaging were drafted, focussing on which findings should prompt recommendations for further workup. These recommendations discuss how to deal with incidental marrow changes, focal bone lesions, abnormalities of the pubic symphysis and sacroiliac joints, fatty soft tissue masses, manifestations of renal osteodystrophy and finally discuss opportunistic osteoporosis evaluation.

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)0.007
metaresearch head score (Gemma)0.041
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.059
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.002
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0040.002
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0120.006

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 head0.023
GPT teacher head0.308
Teacher spread0.285 · 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
GenreMethods

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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Association of Radiologists JournalSame topicHip disorders and treatmentsFrench-language works237,207