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Record W4414903406 · doi:10.1055/s-0045-1802976

Common Mistakes in Imaging: Ligament Injuries of the Knee in Athletes

2025· review· en· W4414903406 on OpenAlexaff
Linda Probyn, Dyan V. Flores, Mini Pathri, Christopher F. Beaulieu, Mark Cresswell, Angela Atinga

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2025
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of British ColumbiaUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsMagnetic resonance imagingLigamentAthletesRadiographySports medicineSoft tissueMedical imaging

Abstract

fetched live from OpenAlex

Assessment of internal derangement is a common indication for imaging of the injured knee in athletes. The first line of imaging is conventional radiography, but magnetic resonance imaging is often required. Radiographic features of ligament injury can be subtle, even when the soft tissue injury is devastating, resulting in instability that may require surgery. Magnetic resonance imaging to assess for ligament injury has several potential pitfalls that can lead to interpretation errors. This article describes common errors when imaging knee ligament injuries in the athlete and discusses strategies to reduce inaccuracies in imaging technique and interpretation. Mistakes on magnetic resonance imaging and radiographs typically arise from the timing of imaging (early/acute versus delayed/chronic), technical factors, potential mimics of pathology, and the inherent limitations of radiography.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.010
GPT teacher head0.330
Teacher spread0.320 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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