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Record W4399017700 · doi:10.1177/03635465241248642

Quantitative MRI Analysis of Patellofemoral Joint Cartilage Health 2 Years After Anterior Cruciate Ligament Reconstruction and Lateral Extra-Articular Tenodesis

2024· article· en· W4399017700 on OpenAlexaff
Yuta Nakanishi, Paul Hegarty, Thiago Vivacqua, Andrew Firth, Jaques S. Milner, Stephany Pritchett, Kevin Willits, Robert Litchfield, Dianne Bryant, Alan Getgood

Bibliographic record

VenueThe American Journal of Sports Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsMcMaster UniversityRobarts Clinical TrialsImpactFowler Kennedy Sport Medicine ClinicWestern University
Fundersnot available
KeywordsMedicineAnterior cruciate ligament reconstructionCartilageMagnetic resonance imagingOsteoarthritisPatellaSurgeryHamstringAnterior cruciate ligamentRandomized controlled trialRadiologyAnatomy

Abstract

fetched live from OpenAlex

Background: The addition of an iliotibial band–based lateral extra-articular tenodesis (LET) to anterior cruciate ligament (ACL) reconstruction (ACLR) has been shown to reduce failure rates. However, there are concerns as to the potential overconstraint of tibiofemoral kinematics that may increase the risk of cartilage degradation. To date, no clinical study has investigated the effect of LET on patellofemoral joint articular cartilage health. Hypothesis: It was hypothesized that at 2 years postoperatively, (1) the addition of LET at the time of ACLR would have no effect on cartilage health on magnetic resonance imaging (MRI), and (2) higher cartilage relaxation values would be associated with worse patient-reported and functional outcomes. Study Design: Cohort study; Level of evidence, 3. Methods: A subset of patients from the STABILITY 1 randomized controlled trial were included. All patients underwent primary ACLR with a hamstring autograft. Patients were randomized to either LET augmentation or not. Cartilage status in the patellofemoral joint between the ACLR group and ACLR+LET group was compared using 2-year postoperative quantitative MRI and the ACL osteoarthritis scores of both the surgical and the contralateral nonsurgical knees. Objective functional outcomes and patient-reported outcome measures (PROMs) were attained. Results: A total of 92 patients (43 patients in the ACLR group; mean age, 18.9 ± 3.2 years; 60.5% female; and 49 patients in the ACLR+LET group; mean age, 18.7 ± 3.2 years, 63.3% female) were included. No significant differences were seen in the mean values (ms) for adjusted T1ρ/T2 relaxation times in the medial patella (47.8/42.2 vs 47.3/43.2), central patella (45.5/42.5 vs 44.1/42.7), lateral patella (48.2/43.5 vs 47.3/43.0), medial trochlea (54.7/50.9 vs 56.4/50.9), central trochlea (53.3/51.1 vs 53.1/52.0), and lateral trochlea (54.9/52.1 vs 53.9/52.6) between the ACLR and ACLR+LET groups. No difference in overall ACL osteoarthritis scores was observed ( P = .99). An increase in medial patellar T2 relaxation times was associated with a decreasing International Knee Documentation Committee score ( P = .046), Knee injury and Osteoarthritis Outcome Score (KOOS) Symptoms subscale score ( P = .01), and total KOOS ( P = .01). Conclusion: There was no statistical difference in patellofemoral cartilage health between knees 2 years after primary ACLR with hamstring tendon autograft with or without LET. Statistically significant correlations were found between quantitative MRI relaxation times, functional outcome scores, and PROMs; however, the correlations were weak and the clinical significance is unknown. Registration: NCT02018354 (ClinicalTrials.gov identifier).

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.013
GPT teacher head0.298
Teacher spread0.285 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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