Evidence Mapping Based on Systematic Reviews of Cognitive Behavioral Therapy for Neuropathic Pain
Bibliographic record
Abstract
Objective: This evidence mapping is aimed at identifying, summarizing, and analyzing the available evidence on cognitive behavioral therapy (CBT) for neuropathic pain (NP). Methods: This study was conducted following the methodology of Global Evidence Mapping (GEM). Searches were conducted in PubMed, Embase, the Cochrane Library, and PsycINFO to identify systematic reviews (SRs) with or without meta-analysis published before February 15, 2022. The authors independently assessed eligibility, extracted data, and evaluated the methodological quality of the included SRs using AMSTAR-2. The results were presented in the tables and a bubble plot based on the identified population-intervention-comparison-outcome (PICO) questions. Results: A total of 34 SRs met the eligibility criteria. According to the AMSTAR-2, 2 SRs were rated "high," 2 SRs were rated "moderate," 6 SRs were rated "low," and 24 SRs were rated "critically low." The most common study design utilized to evaluate the efficacy of CBT for NP was the randomized controlled trial. In total, 24 PICOs were identified. Migraine was the most studied population. CBT for NP usually reaches the "potentially better" result at follow-up. Conclusions: Evidence mapping is a useful way to present existing evidence. Currently, the existing evidence on CBT for NP is limited. Overall, the methodological quality of the included SRs was low. Further improvements in the methodological quality of SRs and more research on the most efficient CBT formats for NP are recommended in the future.
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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 | 0.118 | 0.391 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.075 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".