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Record W2136703326 · doi:10.1136/bjsm.2010.076695

Get a kick out of this: the spectrum of knee extensor mechanism injuries

2010· article· en· W2136703326 on OpenAlexaff
Betty Tuong, Jeremy J. White, Luck J. Louis, Robyn Cairns, Gordon Andrews, Bruce B. Forster

Bibliographic record

VenueBritish Journal of Sports Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of British Columbia Hospital
Fundersnot available
KeywordsMedicineChondromalaciaModalitiesTendinopathyPatellaBroad spectrumSports injuryMechanism (biology)UltrasoundPhysical medicine and rehabilitationPhysical therapyRadiologyAnatomyTendon

Abstract

fetched live from OpenAlex

At the end of this article, the reader should be able to (1) recognise normal anatomy and anatomical variants of the extensor mechanism of the knee on various imaging modalities, including plain film, ultrasound and MRI; (2) diagnose a broad spectrum of EM injuries in adult and paediatric patients including patellar and quadriceps tendinopathy, Osgood-Schlatter disease, Sindig-Larsen-Johansson syndrome, chondromalacia patellae and patellar fractures on various imaging modalities; and (3) appreciate the important role of imaging in the diagnosis of musculoskeletal injuries.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.007
GPT teacher head0.253
Teacher spread0.246 · 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

Citations26
Published2010
Admission routes1
Has abstractyes

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