Stratification of flare intensity identifies placebo responders in a treatment efficacy trial of patients with osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Studies evaluating osteoarthritis treatment often use increased arthritis activity ("flare") as a selection criterion, although no standardized assessments are available to quantify flare intensity and little is known about how this criterion affects treatment comparisons. This study evaluated the reliability of a flare assessment and how pretreatment flare intensity impacts conclusions on treatment efficacy. METHODS: Using data from a double-blind, randomized, controlled trial (n = 182), we compared 3 osteoarthritis treatments with placebo in patients who met 3 of 4 flare criteria. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire was used to document levels of pain, stiffness, and physical functioning at baseline and at the final visit. Following factor analytic evaluation, the flare items were standardized and summed to create a flare intensity index, which was used to identify patient subgroups. Analysis of covariance was applied to compare change in WOMAC scale scores from baseline to final visit for assessment of treatment differences among the flare intensity subgroups. RESULTS: The flare indicators appeared unidimensional. Analyses were stratified by tertiles of flare intensity. Mean WOMAC scores improved in the patients receiving active treatment who were categorized into the 2 lowest flare intensity subgroups, but mean WOMAC scores improved in patients in all 4 treatment groups (active and placebo) in the most intense flare subgroup. CONCLUSION: Patients with higher intensity flares may be more likely to report substantial improvement in functional status regardless of treatment. Failure to account for flare intensity in analyses of data from pain trials with flare-based designs may inflate the risk of Type I and Type II errors in the interpretation of study results.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".