Psychopathological and sociodemographic features in treatment-resistant unipolar depression versus bipolar depression: a comparative study
Bibliographic record
Abstract
BACKGROUND: Some authors have hypothesized that Treatment-Resistant Unipolar Depression (TRD-UP) should be considered within the bipolar spectrum disorders and that hidden bipolarity may be a risk factor for TRD-UP. However, there are neither studies comparing clinical and sociodemographic data of patients with TRD-UP versus Bipolar (BP) disorders nor are there any examining differences versus Bipolar type I (BP-I) and Bipolar type II (BP-II). METHODS: Charts analysis was conducted on 194 patients followed at the Mood Disorders Clinic of the McGill University Health Center. Sociodemographic, clinical features and depression scales were collected from patients meeting DSM-IV criteria for TRD-UP (n = 100) and BP (n = 94). Binary logistic regression analysis was conducted to examine clinical predictors independently associated with the two disorders. RESULTS: Compared to BP, TRD-UP patients exhibited greater severity of depression, prevalence of anxiety and panic disorders, melancholic features, Cluster-C personality disorders, later onset of depression and fewer hospitalizations. Binary logistic regression indicated that higher comorbidity with anxiety disorders, higher depression scale scores and lower global assessment of functioning (GAF) scores, and lower number of hospitalizations and psychotherapies differentiated TRD-UP from BP patients. We also found that the rate of unemployment and the number of hospitalizations for depression was higher in BP-I than in BP-II, while the rate of suicide attempts was lower in BP-I than in BP-II depressed patients. CONCLUSIONS: These results suggest that TRD-UP constitutes a distinct psychopathological condition and not necessarily a prodromal state of BP depression.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".