Efficacy of Psychological Treatment for Headaches
Bibliographic record
Abstract
OBJECTIVES: A variety of psychological treatments exist for headaches (HAs). Their efficacy has been evaluated through systematic reviews with meta-analysis. Our goal was to evaluate the scope of these reviews and reevaluate the efficacy of treatments considering potential sources of variation systematically. These findings should help guide clinical practice and will provide guidance to researchers planning to address the efficacy of psychological treatments for HAs. MATERIALS AND METHODS: Two systematic reviews were conducted: one searched for systematic reviews with meta-analysis exploring the efficacy of psychological treatments for HA in Cochrane Database, DARE, EMBASE, ISI Web of Knowledge, Medline, and PsychINFO from inception to December 2011. Two independent reviewers screened, evaluated quality, and extracted data. The second review searched for primary studies from the included reviews estimating the efficacy of psychological treatments for a clinically significant change. RESULTS: Eighteen reviews met a priori criteria for inclusion. The broad scope of research on efficacy of psychological treatments for HA is reflected by variation in clinical and methodological characteristics of the reviews. These variations were explored through meta-analysis and subgroup analysis of 41 primary studies and showed that some of these variations, including time of assessment, treatment type, age, HA diagnosis, and study quality, can impact the magnitude of treatment effect. DISCUSSION: There is substantial evidence in favor of psychological treatments for HA management. Further investigation, especially in specific treatments (cognitive-behavioral or autogenic treatment) for HA disorders, is needed. The assessment of these systematic reviews highlighted key areas where improvement should be made to increase the quality of evidence.
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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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".