Atypical depression and emotion dysregulation: Clinical and psychopathological features
Bibliographic record
Abstract
Background Most atypical depression (AD) cases endorse prominent mood reactivity, anxiety, and interpersonal sensitivity, resembling some of the characteristics of emotional dysregulation (ED). The present study assesses the frequency and clinical features of different levels of ED in AD yes vs. non-AD(AD no ) cases. Methods The present cross-sectional study discriminated depressed outpatients screened with the Hamilton Depression rating scale with the Atypical Depression Supplement (SIGH-ADS), Symptom Checklist-90-Revised, Temperament Evaluation of Memphis, Pisa, Paris, and San Diego Auto-questionnaire, 110-item version, 36-item Difficulties in Emotion Regulation Scale (DERS), and Young Mania Rating Scale into people with high (ED high ) vs. low (ED low ) for a broad range of clinical and psychopathological features. Descriptive statistics were followed by random forest analysis with “out-of-bag”[OOB] computation. Results We included 326 patients (MDD = 204[62.60 %], BD-II = 105[32.20 %], and BD- I = 17[5.20 %]). AD yes ED high cases had the earliest age at the onset of depression and overall clinical burden. Higher scores at interpersonal sensitivity, somatization , early age at onset of depression, anxious features, non-atypical core of depression, cyclothymic and depressive temperament, DERS total, and strategies scores predicted higher odds of atypical depression (OOB = 0.25). Among other predictors, age at onset of depression somatization and cyclothymic temperament predicted ED high group membership (OOB = 0.23). Hyperthymic temperament, the SIGH-ADS atypical balance percentage score, and somatization emerged as top predictors of treatment-resistant-depression (OOB = 0.12) in contrast to the SIGH-ADS-8-item atypical balance, psychotic features, and age at onset for treatment-resistant-bipolar-depression (OOB = 0.16). Limitations Cross-sectional design; treatment-seeking outpatients. Conclusions: AD and ED represent intertwined clinical entities potentially relevant to enhanced treatment outcomes, warranting more accurate random-forest models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".