The Relationship of Pain Reduction With Prevention of Knee Replacement Under Dynamic Intervention Strategies
Bibliographic record
Abstract
OBJECTIVE: Knee replacement (KR) rates are increasing exponentially in the US and straining insurance budgets. This study was undertaken to investigate how many KRs would be prevented at different levels of pain improvement, a major target of osteoarthritis (OA) trials. METHODS: We used data from the Osteoarthritis Initiative (OAI) to emulate a trial of knee pain interventions on KR risk changes. We modeled hypothetical 1-, 2- or 3-unit reductions of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale whenever a person reported a pain score of ≥5 (of 20) in an affected knee at any clinic visit. We used causal inference-based targeted learning to estimate treatment effects for hypothesized pain intervention strategies adjusted for time-dependent confounding. Sensitivity analyses assessed interventions at WOMAC pain scores of ≥4 and ≥7. RESULTS: Of the 9,592 knees studied (n = 4,796 participants; 58.5% female; baseline age 61.2 years), 40.7% experienced WOMAC pain scores of ≥5. The estimated knee-level (reference) risk of a KR, adjusted for loss to follow-up and death, was 6.3% (95% confidence interval 5.0, 7.7%) in the OAI. Reductions of WOMAC pain scores by 1, 2, or 3 units decreased the KR risk from 6.3% to 5.8%, 5.3%, and 4.9%, respectively. Larger reductions in KR risk were achieved when interventions were applied at a WOMAC pain score of ≥4. CONCLUSION: Modest pain reductions from OA interventions would substantially reduce the number of KRs, with greater reductions achieved when pain decreased more and when interventions were introduced at lower pain levels.
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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 | 0.019 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".