Review: some evidence suggests that cognitive behaviour therapy may reduce chest pain in the short term in patients with non-specific chest pain and normal coronary anatomy
Bibliographic record
Abstract
Kisely S, Campbell LA, Skerritt P. Psychological interventions for symptomatic management of non-specific chest pain in patients with normal coronary anatomy. Cochrane Database Syst Rev 2005;(1):CD004101. Q Are psychological treatments effective for patients with non-specific chest pain and normal coronary anatomy? ### ![Graphic][1] Data sources: Cochrane Review Group Specialised registers (November 2002); Cochrane Library (Issue 3, 2002); Medline (1966–2002); CINAHL (1982–2002); EMBASE/Excerpta Medica (1980–2002); PsycLIT (1887–2002); Biological Abstracts BIOSIS (1980–2002); reference lists of relevant studies and reviews; abstracts from cardiology, psychiatry, and psychiatry conferences; and personal communication with authors. ### ![Graphic][2] Study selection and assessment: randomised controlled trials (RCTs) that compared psychological interventions (cognitive behaviour therapy [CBT], relaxation therapy, hyperventilation control, or other psychotherapy, talking, or counselling therapy) with standard care, an attention placebo, or no intervention in patients with non-specific chest pain, atypical chest pain, or syndrome X and normal coronary anatomy. Patients receiving drug treatment for psychiatric disorders were excluded. Individual study quality was assessed based on … [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".