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Record W2990801369 · doi:10.1093/pm/pnz320

Ultrasonographic and Magnetic Resonance Images of Semimembranosus-Tibial Collateral Ligament Bursitis

2019· article· en· W2990801369 on OpenAlexaff
Min Cheol Chang, Mathieu Boudier‐Revéret, Wen‐Shiang Chen

Bibliographic record

VenuePain Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal synovial abnormalities and treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineBursitisMagnetic resonance imagingLigamentMedial collateral ligamentCollateralRadiologyAnatomy

Abstract

fetched live from OpenAlex

A 31-year-old woman presented with pain and swelling in the posteromedial area of her right knee. She had worked as a farmer for 10 months and did not have a history of trauma. On physical examination, deep palpation revealed tenderness along the right posteromedial knee. Ultrasound (US) imaging (12-MHz linear probe, Toshiba, Aplio 500) revealed an anechoic, fluid-filled, distended sac (7.4 × 14.4 × 38.9 mm) that surrounded the superior, medial, and inferior portions of the right semimembranosus (SM) tendon without an increase in vascularity (Figure 1A, B). The sac consisted of deep and superficial pockets. The deep pocket was located between the SM tendon and the posteromedial aspects of the medial meniscus and medial tibial condyle. The superficial pocket was situated between the SM tendon and the tibial collateral ligament (TCL), also known as the medial collateral ligament (MCL). Considering the location and appearance of the fluid sac, it was compatible with a semimembranosus-tibial collateral ligament (SMTCL) bursitis (Supplementary Data) [1]. Confirmatory magnetic resonance imaging (MRI) showed fluid over the right SM tendon in a shape similar to an inverted “U,” with the proximal deep and distal superficial pockets forming the two arms of the “U” (Figure 2A–C). These findings indicate SMTCL bursitis [2].

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.225
Teacher spread0.220 · 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 teacher head, not a consensus.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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