Placebo response in neuropathic pain after spinal cord injury: a meta-analysis of individual participant data
Bibliographic record
Abstract
Background: Understanding factors associated with high placebo responses in clinical trials increases the likelihood of detecting a meaningful treatment effect. The aim of the present study was to identify subject-level factors that contribute to placebo variability in patients with neuropathic pain due to spinal cord injury (SCI). Methods: Multiple regression analysis of patient data from randomized, double-blind, placebo-controlled trials (duration >4 weeks) involving individuals with SCI was performed. Patient demographics, as well as injury and pain characteristics were examined for their association with changes in pain rating from baseline to the end of the trial (i.e., placebo response). The overall effect of individual predictors was quantified with meta-analysis statistics. Results: A total of 276 patients with SCI from six studies were included in the analysis. Based on the meta-analysis of subject-level predictors, larger placebo responses were associated with male subjects (β=0.635; standard error [SE]=0.262; p =0.016) and higher baseline pain (β=−0.146; SE=0.073; p =0.044). There were no significant effects for injury characteristics (i.e., severity, level, and time since injury) or pain characteristics (i.e., location and evoked). No significant publication bias was detected. Conclusion: The current meta-analysis of individual patient data demonstrated the importance of sex and baseline pain intensity on changes in pain ratings in the placebo arm of SCI central neuropathic pain randomized controlled clinical trials. Overall, our findings indicate that placebo responses occur independent of injury characteristics. Keywords: placebo response, clinical trial, spinal cord injury, neuropathic 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 | 0.066 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.059 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".