Further Qualification of a Therapeutic Responder Index for Patients with Chronic Low Back Pain
Bibliographic record
Abstract
OBJECTIVE: Previously, a preliminary patient responder index (RI) in chronic low back pain (CLBP) was developed and validated in 5 placebo-controlled clinical trials. The resulting RI was a > 30% improvement in CLBP and patient global assessment (PGA), and no worsening (< 20%) in the Roland Morris Disability Questionnaire (RMDQ) total score. Our objective was to provide further characterization of the preliminary RI in a trial with an active control. METHODS: Data from a 6-week randomized, double-blind study of celecoxib compared to tramadol hydrochloride was analyzed to determine differences by treatment group on the CLBP RI and its components, to compare the CLBP RI with each of its individual components, and to reanalyze the original cutoff points for the responder criteria. RESULTS: Of the celecoxib arm, 50.7%, and of the tramadol hydrochloride arm, 43.7% were classified as responders under the CLBP RI (p = 0.043). The PGA is the most important component in the RI (45% of the sample failed to reach the > 30% improvement criteria on the PGA compared to 34% on the low back pain visual analog scale and only 11% on the RMDQ. The agreement among the CLBP RI with each of its 3 components was largest for the PGA component (κ coefficient 0.849) and smallest for the RMDQ component (κ coefficient 0.207). CONCLUSION: The RI appears to be particularly sensitive to the cutoff point used for improvement in the PGA component. Further testing of the index in trials with other active comparators is required to gain a fuller understanding of its performance.
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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 | 0.050 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".