The Impact of Therapy on Quality of Life and Mood in Neuropathic Pain: What Is the Effect of Pain Reduction?
Bibliographic record
Abstract
In Brief Mood and quality of life (QOL) outcomes vary widely in neuropathic pain trials. This may be a result of variable analgesia and other treatment effects. We evaluated the relationship between pain reduction and mood/QOL in neuropathic pain. Pain, side effects, QOL, and mood from a trial of morphine, gabapentin, and a morphine-gabapentin combination were examined. Baseline QOL was impaired according to Short Form Health Survey (SF-36) scores. Baseline mood, according to Profile of Mood States scores, was comparable to that of a nondepressed population. Pain reduction with all three active trial treatments correlated with improved QOL. Pain reduction with morphine and with gabapentin correlated with improved mood. Pain reduction with a morphine-gabapentin combination correlated with improvement in only one of several domains of the Profile of Mood States. Severity of sedation, constipation, and dry mouth during any treatment did not correlate with mood/QOL changes. These results can be interpreted to imply that larger analgesic treatment effect sizes lead to more substantial improvements in QOL and/or mood. However, other beneficial or adverse treatment-related side effects may also affect mood/QOL. Therefore, future studies are needed to also evaluate the impact of treatment-related side effects on mood/QOL in analgesic trials. IMPLICATIONS: We evaluated the relationship between analgesia and mood/quality of life using outcomes from a recent clinical trial. Treatment-induced analgesia was shown to correlate with improvement in mood and quality of life. These results suggest that greater analgesic efficacy may lead to larger improvements in mood/quality of life.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".