Predictive Validity of Radiographic Trabecular Bone Texture in Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate radiographic subchondral trabecular bone texture (TBT) as a predictor of clinically relevant osteoarthritis (OA) progression (combination of symptom and structural worsening). METHODS: The Foundation for the National Institutes of Health (FNIH) OA Biomarkers Consortium undertook a study of progressive knee OA cases (n = 194 knees with both radiographic and pain progression over 24-48 months) and comparators (n = 406 OA knees not meeting the case definition). TBT parameters were extracted from a medial subchondral tibial region of interest by fractal signature analysis of radiographs using validated semiautomated software. Baseline TBT and time-integrated values over 12 and 24 months were evaluated for association with case status and separately with radiographic and pain progression status, adjusted for age, sex, body mass index, race, baseline Kellgren/Lawrence grade, baseline joint space width, Western Ontario and McMaster Universities Osteoarthritis Index pain score, and pain medication use. C statistics were generated from receiver operating characteristic curves. RESULTS: Relative to comparators, cases were characterized by thinner vertical and thicker horizontal trabeculae. The summed composite of 3 TBT parameters at baseline and over 12 and 24 months best predicted case status (odds ratios 1.24-1.43). The C statistic for predicting case status using the TBT composite score (0.633-0.649) was improved modestly but statistically significantly over the use of covariates alone (0.608). One TBT parameter, reflecting thickened horizontal trabeculae in cases, at baseline and over 12 and 24 months, predicted risk of any progression (radiographic and/or pain progression). CONCLUSION: Although associations are modest, TBT could be an attractive means of enriching OA trials for progressors since it can be generated from screening knee radiographs already standard in knee OA clinical trials.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".