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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".