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Record W2903050074 · doi:10.1002/jum.14853

Validity of Indirect Ultrasound Findings in Acute Anterior Cruciate Ligament Ruptures

2018· article· en· W2903050074 on OpenAlexaff
Kenneth Mautner, Walter I. Sussman, Katie Nanos, Joe Blazuk, Carmen Brigham, Emily Sarros

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsU SPORTS
Fundersnot available
KeywordsMedicineAnterior cruciate ligamentTearsUltrasoundPosterior cruciate ligamentMagnetic resonance imagingPhysical examinationLigamentACL injuryRadiologySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: Ultrasound (US) is increasingly being used as an extension of the physical examination on the sidelines, in training rooms, and in clinics. Anterior cruciate ligament (ACL) injury in sport is common, but the literature on US findings after acute ACL rupture is limited. Three indirect US findings of ACL rupture have been described, and this study assessed the validity of these indirect signs. METHODS: Patients with an acute knee injury (<6 weeks) underwent US examinations to determine whether there was evidence of a femoral notch sign, posterior cruciate ligament wave sign, or capsular protrusion sign. Ultrasound findings were compared to magnetic resonance imaging. RESULTS: Sixty-nine patients were included (53 with ACL tears and 16 control patients). The posterior cruciate ligament sign had the highest sensitivity (84.9%), and the notch sign had the highest specificity (93.8%). If 2 or 3 of the signs were positive, the sensitivity was 86.8%, and the specificity was 87.5%. CONCLUSIONS: A US examination is an easy-to-perform and noninvasive test, and the 3 indirect signs of an acute ACL tear had high positive predictive values ranging from 91.8% to 96.8%.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.165
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.324
Teacher spread0.305 · 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.

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

Citations11
Published2018
Admission routes1
Has abstractyes

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