Sensory and Affective Pain Descriptors Respond Differentially to Pharmacological Interventions in Neuropathic Conditions
Bibliographic record
Abstract
OBJECTIVES: Pain management is limited by inability to match a patient's condition-and pain mechanisms-to optimal treatment(s). Much is known about pain treatment from animal investigations, but antinociceptive mechanisms cannot be readily explored in clinical studies. Evidence suggests that self-report verbal pain descriptors characterize important pain dimensions and may reflect diverse underlying mechanisms. METHODS: This exploratory analysis of data from a trial of a gabapentin-morphine combination evaluated effects of treatment on short-form McGill Pain Questionnaire sensory and affective descriptor profiles and prediction of treatment response by these descriptors. RESULTS: Severity of "throbbing," "shooting," and "aching" improved preferentially with morphine over gabapentin, whereas "tiring-exhausting" and "sickening" improved preferentially with gabapentin over morphine. Improvement in descriptor severity with gabapentin-morphine combination was superior to active placebo for 12 of 15 short-form McGill Pain Questionnaire descriptors, whereas morphine and gabapentin were superior to active placebo for only 7 and 6 descriptors, respectively. Baseline moderate-severe "throbbing" and "hot-burning" predicted poor outcomes with gabapentin, whereas moderate-severe "aching" and "punishing-cruel" predicted favorable outcomes with gabapentin. Baseline "throbbing" severity also predicted poor outcomes with morphine. Baseline allodynia predicted superior reduction of "stabbing" with morphine but not with gabapentin alone. DISCUSSION: These results point to the hypothesis that sensory and affective pain descriptor profiles exhibit a treatment-specific response. Larger, more definitive, investigations to evaluate treatment-specific effects on multiple sensory and affective pain descriptors, and prediction of treatment response by these descriptors, will advance efforts toward developing and implementing more effective individualized pain therapies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".