Bisphosphonate Use Is Protective of Radiographic Knee Osteoarthritis Progression Among those With Low Disease Severity and Being Non-Overweight: Data From the Osteoarthritis Initiative
Bibliographic record
Abstract
ABSTRACT Antiresorptive medications have been explored for treating knee osteoarthritis (OA); however, little data exist on the effects of today's more potent nitrogen-containing oral bisphosphonates on radiographic disease-progression in patients with varying disease-severity, especially those who are not overweight. The primary objective of this cohort study was to determine if the use of bisphosphonates is protective against 2-year radiographic-progression of knee OA in Osteoarthritis Initiative (OAI) participants, stratified by baseline radiographic disease status. Secondary objectives were to examine effects in non-overweight participants (body mass index [BMI] < 25 kg/m2) and cumulative bisphosphonate exposure effects. We identified female OAI participants aged ≥50 years and excluded those missing baseline radiograph readings, bisphosphonate use information, or all clinical questionnaire information at baseline. Participants reporting bisphosphonate use (69% alendronate) were propensity-matched 1:1 to non–bisphosphonate users and followed until first radiographic knee OA progression (1-unit increase in Kellgren and Lawrence [KL] grade) or data were censored (first missed visit or end of 2-year follow-up). Discrete-time logistic regression models estimated hazard ratios (HRs) between bisphosphonate users versus nonusers, with an interaction term for baseline KL grade (KL <2 or KL ≥2). We identified 1977 eligible women (n = 346 bisphosphonate users). Propensity-matched results indicated that bisphosphonate users with KL grade <2 were protected against progression (HRKL<2 0.53; 95% CI, 0.35 to 0.79), while bisphosphonate use was not associated with radiographic progression in those with KL grade ≥2 (HRKL≥2 1.06; 95% CI, 0.83 to 1.35). When restricting analyses to those with BMI <25 kg/m2, effects were strengthened (HRKL<2 0.49 [95% CI, 0.26 to 0.92]; HRKL≥2 0.69 [95% CI, 0.33 to 1.26]). Duration of bisphosphonate use had no effect on progression, though sample size was limited. Bisphosphonate therapy may be protective against radiographic knee OA progression in early-stage patients, particularly those who are non-overweight, but less so for those with more advanced disease or more weight-bearing joint stress. © 2020 American Society for Bone and Mineral Research (ASBMR).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".