Symptom network connectivity indices as predictors of relapse in major depressive disorder
Bibliographic record
Abstract
Major Depressive Disorder (MDD) demonstrates heterogeneous symptom profiles and a high relapse risk. Understanding how symptom interactions relate to relapse in MDD may enhance maintenance strategies. We thus investigated how connectivity in depressive symptom networks relates to relapse in MDD. We analyzed longitudinal data from 87 patients with remitted MDD who were followed for an average of 12 months. Patient-level symptom networks were estimated using multilevel graphical vector autoregression applied to weekly self-ratings of the Quick Inventory of Depressive Symptomatology. We calculated patient-specific network connectivity indices, including symptom network density (SND), vertex cover (VC) size, and minimal dominating set (MDS) size. Cox proportional hazards models assessed associations between these indices and time to relapse, controlling for baseline symptom severity. Higher SND and larger VC size were significantly associated with an increased relapse risk (HR for SND = 2.03, 95 % CI [1.44, 2.87], p < 0.001; HR for VC = 2.77, 95 % CI [1.79, 4.30], p < 0.001). Conversely, a larger MDS size was associated with a lower risk of relapse (HR 0.47, 95 % CI [0.31, 0.70], p < 0.001). Exploratory analyses showed that the strength centrality of sadness, difficulty concentrating, pessimism, suicidality, and low interest portend a higher relapse risk. Hyperconnectivity among depressive symptom networks may indicate vulnerability to relapse in MDD. Our results further support the potential utility of symptom network-based analyses for predicting outcomes in MDD. Future studies should evaluate whether symptom network-based analyses can provide markers for personalized treatment planning.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".