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Record W4410998322 · doi:10.1530/eor-2025-0051

Patellar instability: current approach

2025· article· en· W4410998322 on OpenAlex
David Mazy, Tomás Pineda, Nicolas Cance, Michael J. Dan, Edoardo Giovannetti de Sanctis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEFORT Open Reviews · 2025
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPatellaPatellofemoral jointMedicineTibial tuberosityValgusDeformityDysplasiaOrthodonticsInstabilityValgus deformityPatellar ligamentSurgeryPatellar tendonAnterior cruciate ligamentInternal medicinePhysics

Abstract

fetched live from OpenAlex

Patellar dislocations present predominantly during adolescence, with a higher incidence observed among female patients. Patellofemoral joint stability depends critically on both osseous anatomy and soft tissue structures. Patellofemoral pathology can be classified into three major groups: objective patellar instability OPI, potential patellar instability and painful patellar syndrome. Three primary risk factors predispose individuals to patellar dislocation: trochlear dysplasia, patella alta and increased tibial tuberosity-trochlear groove (TT-TG) distance. Three secondary risk factors should be considered: femoral and tibial rotational abnormalities and valgus deformity. MRI has become the imaging modality of choice, enabling precise quantification of OPI risk factors in a single imaging examination. The 'menu à la carte' approach guides the treatment of OPI by addressing the most relevant anatomical risk factors for each patient using statistical thresholds.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.920
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.315
Teacher spread0.255 · 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