Body mass index and sex and their effect on patient-reported outcomes following cartilage repair: an insight from the International Cartilage Regeneration and Joint Preservation Society Patient Registry
Bibliographic record
Abstract
Introduction Chondral injuries in the knee, whether isolated or accompanying other injuries are found in as many as 60% of arthroscopic examinations. Although current research has identified negative outcomes for patients with a body mass index (BMI) >30kg/m 2 undergoing chondral repair, our understanding of the relationship between presurgery BMI and postoperative patient-reported outcomes across all BMI categories remains lacking. Objectives Through the International Cartilage Regeneration and Joint Preservation Society (ICRS) Patient Registry, this study aimed to explore this relationship, taking into account sex variations. Methods The ICRS Patient Registry was used to extract the data for this study. The outcomes in focus were the Knee Osteoarthritis Outcome Score (KOOS) and EQ-5D scores. Pearson and Spearman correlation methods were applied and the level of significance was set as α = 0.05. Results Of 3194 Registry patients at the time of data extraction, 1757 had undergone a surgical procedure, and 336 of these had complete KOOS or EQ-5D scores available for 6-week, 6-month, and 1-year postoperation. Analyses revealed that neither male (average BMI – 28.2 kg/m 2 ) nor female (average BMI – 25.3 kg/m 2 ) data sets indicated a correlation between BMI and the patient-reported outcomes. Conclusions BMI, irrespective of sex, is not correlated with patient-reported outcomes in patients enrolled in the ICRS Registry with a BMI <30 kg/m 2 . Although BMIs in the overweight classification were not associated with poorer outcomes than BMIs in the normal classification, the current literature continues to support the notion that a BMI >30 kg/m 2 is linked to poor cartilage repair and failure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".