Meta-analysis of placebo responses in central neuropathic pain
Bibliographic record
Abstract
The placebo response is a complex construct related to psychobiological effects, as well as natural history and regression to the mean. Moreover, patient and study design characteristics have also been proposed as significantly affecting placebo responses. The aim of the current investigation was to identify factors that contribute to variable placebo responses in clinical trials involving individuals with central neuropathic pain. To this end, we performed a systematic review and meta-analysis of placebo-controlled trials examining pharmacological and noninvasive brain stimulation interventions for central neuropathic pain. Study design, subject characteristics, and pain ratings for the placebo group were extracted from each trial. Pooling of results and identification of moderating factors were carried out using random effects meta-analysis and meta-regression techniques. A total of 39 published trials met the inclusion criteria (spinal cord injury, n = 26; stroke, n = 6; multiple sclerosis, n = 7). No significant publication bias was detected. Overall, there was a significant effect for placebo to reduce central pain (-0.64, CI: -0.83 to -0.45). Smaller placebo responses were associated with crossover-design studies, longer pain duration, and greater between-subject baseline pain variability. There were no significant effects for neurological condition (stroke vs multiple sclerosis vs spinal cord injury) or the type of intervention (eg, pharmacological vs noninvasive brain stimulation). In a planned subanalysis, the severity of damage in the spinal cord also had no significant effect on the placebo response. Further study is warranted to identify factors that may explain the impact of pain duration on the placebo response at the individual subject level.
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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 | 0.044 | 0.083 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.060 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".