Emotional subtypes in patients with depression: A cluster analysis
Bibliographic record
Abstract
Major depressive disorder (MDD) is associated with deficits in emotion experience, expression and regulation. Whilst emotion regulation deficits prolong MDD, emotion expression influences symptomatic presentations, and anticipatory pleasure deficits predict recurrence risk. Profiling MDD patients from an emotion componential perspective can characterize subtypes with different clinical and functional outcomes. This study aimed to investigate emotional subtypes of MDD. A two-stage cluster analysis applied to 150 MDD patients. Clustering variables included emotion experience measured by Temporal Experience of Pleasure Scale, emotion expression measured by Toronto Alexithymia Scale, and emotion regulation measured by Emotion Regulation Questionnaire. We validated the resultant clusters by comparing their symptoms and functioning with that of 50 controls. Cluster 1 (n = 50) exhibited intact emotion experience and expression yet adopted reappraisal rather than suppression strategy, whereas Cluster 2 (n = 66) exhibited generalized emotional deficits. Cluster 3 (n = 34) exhibited emotion expression deficits and adopted both reappraisal and suppression strategies. On validation, Cluster 2 exhibited the worst, but Cluster 1 exhibited the least symptoms and social functioning impairments. Cluster 3 was intermediate among the two other subtypes. Our findings support the existence of different emotional subtypes in MDD patients, and have clinical and theoretical implications for developing future specific treatments for MDD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".