Do rates of femorotibial cartilage loss in Kellgren-Lawrence 2 and 3 knees differ between those with mild-moderate vs. severe patellofemoral structural damage? – Data from the FNIH and IMI-APPROACH cohorts
Bibliographic record
Abstract
BACKGROUND: The aim was to assess whether rates of quantitative femorotibial (FT) cartilage loss are increased for knees with semiquantitatively (sq)-defined severe patellofemoral (PF) cartilage damage and/or large bone marrow lesions (BMLs) vs. those without over a period of 24 months. METHODS: 626 knees with Kellgren-Lawrence 2 and 3 from the FNIH and IMI-APPROACH studies were included. MRI assessment was performed using the MRI Osteoarthritis Knee Score (MOAKS) instrument. Baseline FT cartilage damage severity was defined as mild, moderate, or severe. PF cartilage damage was defined as mild-moderate vs. severe. A 2nd definition was based on the presence or absence of large BMLs. Quantitative cartilage thickness loss (defined as the difference from baseline to follow-up in mean cartilage thickness in the medial and in the lateral femorotibial joint, which were computed by summing the cartilage thickness measures observed in the respective cartilage plates) was derived from baseline and 24-month manual segmentations. Between-group comparisons were performed using analysis of covariance (ANCOVA) adjusting for age, sex and body mass index. RESULTS: 410 (65%) knees were categorized as mild, 92 (15%) as moderate, and 124 (20%) as severe medial FT cartilage damage. For almost all categories of FT cartilage damage, the difference in quantitative medial FT cartilage loss was not statistically significant. Only for the category of knees with moderate medial FT cartilage damage, statistically higher rates of FT cartilage loss were observed for those with large PF BMLs compared to those without (mean adjusted difference -0.128 mm, 95% confidence interval [-0.238, -0.018], p=0.023). CONCLUSIONS: Screening for PF cartilage damage and BMLs does not appear to be required in a disease-modifying OA drug trial.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".