Impact of group transdiagnostic cognitive‐behavior therapy for anxiety disorders on comorbid diagnoses: Results from a pragmatic randomized clinical trial in primary care
Bibliographic record
Abstract
BACKGROUND: Anxiety and depressive disorders are the most common mental illnesses worldwide, with most individuals meeting criteria for more than one diagnosis. Most cognitive-behavioral therapy (CBT) approaches target only one disorder at a time, resulting in the need to treat comorbid diagnoses sequentially. Transdiagnostic CBT protocols have been developed that simultaneously treat principal and comorbid disorders. METHOD: The current study reports on a secondary analysis of data from a pragmatic effectiveness randomized trial of group tCBT in comparison to treatment-as-usual (TAU) in primary care. Of the trial sample of 231 patients, 191 had at least one comorbid diagnosis of clinical severity at T0. RESULTS: Overall rates of comorbidity decreased over time (82.0% at T0, 45.0% at T1, 45.7% at T3) and those receiving tCBT showed a significantly lower rate of comorbidity at T1 (33.7%) than TAU (55.7%) and at T3 (tCBT: 27.9%, TAU: 60.2%). Comorbid diagnosis severity ratings reduced to a significantly greater extent in tCBT than in TAU. CONCLUSIONS: tCBT is effective in promoting remission of and reducing the severity of comorbid diagnoses. Implications for the treatment of whole persons as opposed to specific diagnoses is discussed.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".