An Adaptive Role for Negative Expected Pain in Patients With Neuropathic Pain
Bibliographic record
Abstract
OBJECTIVES: To study the relationship between expected pain and future outcomes along with the moderating effects of expected pain in neuropathic pain patients. METHODS: Study participants were recruited for the Canadian Neuropathic Pain Database. To examine the relationship between expected pain and 6-month pain intensity, pain-related disability, and catastrophizing, multiple regressions were performed. These relationships were adjusted for potential confounding (age, sex, baseline pain intensity, and psychological distress). To evaluate the moderating effect of expected pain on the relationship between baseline pain intensity and 6-month outcomes, pain intensity×expected pain interaction terms were created. RESULTS: Complete data for analysis was available for 560 patients (71%). Expected pain was positively correlated with pain intensity and pain-related disability scores at 6 months. The relationship between baseline pain intensity and 6-month catastrophizing scores was moderated by expected pain (however, despite a similar trend, expected pain did not statistically moderate the relationship between baseline pain intensity and 6-month pain intensity or disability). At higher levels of pain, predicted catastrophizing scores were higher for those with low levels of expected pain than those with high levels of expected pain. An opposite relationship was observed for patients with the lower levels of pain. DISCUSSION: In neuropathic pain patients whose pain does not respond to therapy, high levels of expected pain may relate to relatively lower catastrophizing scores by shifting focus away from futile attempts at "curing" pain toward focusing on achievement of more realistic personal goals.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".