Detecting critical decision points in psychotherapy and psychotherapy + medication for chronic depression.
Bibliographic record
Abstract
OBJECTIVE: We sought to quantify clinical decision points for identifying depression treatment nonremitters prior to end-of-treatment. METHOD: Data came from the psychotherapy arms of a randomized clinical trial for chronic depression. Participants (n = 352; 65.6% female; 92.3% White; mean age = 44.3 years) received 12 weeks of cognitive behavioral analysis system of psychotherapy (CBASP) or CBASP plus an antidepressant medication. In half of the sample, receiver operating curve analyses were used to identify efficient percentage of symptom reduction cut points on the Inventory of Depressive Symptoms-Self-Report (IDS-SR) for predicting end-of-treatment nonremission based on the Hamilton Rating Scale for Depression (HRSD). Sensitivity, specificity, predictive values, and Cohen's kappa for identified cut points were calculated using the remaining half of the sample. RESULTS: Percentage of IDS-SR symptom reduction at Weeks 6 and 8 predicted end-of-treatment HRSD remission status in both the combined treatment (Week 6 cut point = 50.0%, Cohen's κ = .42; Week 8 cut point = 54.3%, Cohen's κ = .45) and psychotherapy only (Week 6 cut point = 60.7%, Cohen's κ = .41; Week 8 cut point = 48.7%, Cohen's κ = .49). Status at Week 8 was more reliable for identifying nonremitters in psychotherapy-only treatment. CONCLUSIONS: Those with chronic depression who will not remit in structured, time-limited psychotherapy for depression, either with therapy alone or in combination with antidepressant medication, are identifiable prior to end of treatment. Findings provide an operationalized strategy for designing adaptive psychotherapy interventions.
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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 | 0.042 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".