The Pain Quality Response Profile of Oxymorphone Extended Release in the Treatment of Low Back Pain
Bibliographic record
Abstract
Objective In controlled trials of analgesics, the primary outcome variable is most often a measure of global pain intensity. However, because pain is associated with a variety of pain sensations, the effects of analgesic treatments on different sensations could go undetected if specific pain qualities are not assessed. This study sought to evaluate the utility of assessing the multiple components of non-neuropathic pain in an analgesic clinical trial. Methods A secondary analysis was performed using data from a clinical trial involving 140 individuals with low back pain who were converted from prestudy opioids to an equianalgesic dose of an extended release (ER) formulation of oxymorphone (OPANA ER), which was then titrated to a stable dose [defined as visual analog scale ≤40 mm (0 to 100 mm) on 3 of 5 consecutive days and requiring ≤2 doses rescue medication]. Stabilized participants were then randomly assigned to continue with either oxymorphone ER or placebo for 12 weeks. A multidimensional measure of pain quality, the Pain Quality Assessment Scale (PQAS), was administered before titration, after titration, and after treatment with oxymorphone ER or placebo. Results Significant pretitration to posttitration decreases were observed in 17 of the 20 PQAS pain descriptor items and all 3 PQAS scales. The largest effects of oxymorphone ER were found on the PQAS intense, unpleasant, deep, aching, and sharp items and the PQAS Paroxysmal and Deep scales. Discussion The results indicate that oxymorphone ER has different effects on different pain qualities of low back pain. The responsivity of the PQAS items and scales to the results of treatment with an effective and generally well-tolerated dose of an analgesic, and the ability of the PQAS items and scales to discriminate between an active analgesic and placebo, support their validity as outcome measures. The findings support the utility of using pain descriptor measures for (1) identifying the effects of pain treatments on different pain qualities and (2) targeting pain treatments to those patients who experience certain types of pain.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".