Which symptoms predict recurrence of depression in women treated with maintenance interpersonal psychotherapy?
Bibliographic record
Abstract
BACKGROUND: Even low levels of residual symptoms are known to increase the risk of relapse and early recurrence of major depression. It is not known if ongoing psychotherapy lessens this risk. We therefore examined the impact of persistent symptoms, including mood, insomnia, and anxiety symptoms, on time to recurrence in women receiving maintenance interpersonal psychotherapy (IPT-M) for recurrent depression. METHODS: We analyzed data on 131 women aged 20-60 from a 2-year randomized trial of weekly versus twice-monthly versus monthly IPT-M. Participants achieved remission with IPT alone (n=99) or IPT plus sequential antidepressant medication (n=32). Medications were tapered before starting maintenance treatment. Residual symptoms were assessed with the Hamilton Rating Scale for Depression (HRSD; total score and subscales); insomnia was also assessed in 76 women with the Pittsburgh Sleep Quality Index (PSQI). Data analyses used Cox proportional hazards regression models. RESULTS: Neither overall burden of residual symptoms (HRSD total score), nor HRSD mood and anxiety subscale scores predicted recurrence during ongoing IPT-M. In contrast, persistent insomnia measured both by the HRSD-17 insomnia subscale and the PSQI predicted recurrence. Women with persistent insomnia who required sequential pharamacotherapy had the highest recurrence rate (65%) compared to women requiring sequential treatment without insomnia (13%), or women who had recovered with IPT alone but had persistent insomnia (21%) or no insomnia (18%). CONCLUSIONS: Persistent insomnia following the recovery from an episode of recurrent major depression is associated with increased risk of recurrence despite maintenance psychotherapy, particularly for those withdrawn from antidepressant medication.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".