Predicting Relapse of Depressive Episodes During Maintenance Treatment: The Canadian Biomarker Integration Network in Depression (CAN-BIND) Wellness Monitoring in Major Depressive Disorder Study: Prédire la rechute d’épisodes dépressifs pendant le traitement d’entretien : Une étude de suivi du bien-être dans les troubles dépressifs majeurs du Réseau canadien d’intégration des biomarqueurs pour la dépression (CAN-BIND)
Bibliographic record
Abstract
BackgroundRelapse rates in major depressive disorder (MDD) remain high even after treatment to remission. Identifying predictors of relapse is, therefore, crucial for improving maintenance strategies and preventing future episodes. Remote data collection and sensing technologies may allow for more comprehensive and longitudinal assessment of potential predictors.MethodsThe Canadian Biomarker Integration Network in Depression Wellness Monitoring for MDD (CBN-WELL) study was a prospective, multicentre observational study with an aim to identify biomarkers associated with relapse in patients on maintenance treatment for MDD. Participants had a DSM-5-TR diagnosis of MDD in remission and a Montgomery-Åsberg Depression Rating Scale (MADRS) score ≤14. Participants remained on their baseline medication regimens and were followed bimonthly for up to 2 years. Relapse criteria included MADRS > 22 for 2 consecutive weeks, suicidality or hospitalization, and initiation or change in medication for worsening symptoms. Data collection included clinical assessments, self-report questionnaires, and remote monitoring using wrist-worn actigraphs and smartphones.ResultsA total of 96 participants had follow-up data. Of these, 28.9% experienced a depressive relapse during the study period, with an average time to relapse of 211 days. Baseline depressive severity, as measured by MADRS, was higher in participants who relapsed compared to those who did not, but few other baseline clinical measures differentiated these groups.ConclusionsIndividuals with MDD in remission continued to have high relapse rates despite maintenance treatment. The paucity of clinical factors that predict relapse underscores the need for biomarkers. The CBN-WELL database can be used for future research to integrate multiple predictive factors and to identify objective measures to predict relapse in individuals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".