Psychotic features, particularly mood incongruence, as a hallmark of severity of bipolar I disorder
Bibliographic record
Abstract
BACKGROUND: The occurrence of psychotic features within mood episodes in patients with bipolar I disorder (BD I) has been associated in some studies with a more severe clinical and socio-professional profile. In contrast, other studies establishing the associations of psychotic features in BD I, and in particular of mood-congruent (MC) and mood-incongruent (MI) features, with clinical characteristics have yielded contradictory results. However, many pre-existing studies have been affected by serious methodological limitations. Using a sample of thoroughly assessed patients with BD I our aims were to: (1) establish the proportion of those with MI and MC features, and (2) compare BD I patients with and without psychotic features as well as those with MI to those with MC features on a wide array of socio-demographic and clinical characteristics including course, psychiatric comorbidity and treatment. METHODS: A sample of 162 treated patients with BD I (60.5% female, mean age = 41.4 (s.d: 10.2) years) was recruited within a large family study of mood disorders. Clinical, course and treatment characteristics relied on information elicited through direct diagnostic interviews, family history reports and medical records. RESULTS: (1) A total of 96 patients (59.3%) had experienced psychotic features over their lifetime. Among them, 44.8% revealed MI features at least once in their lives. (2) Patients with psychotic features were much less likely to be professionally active, revealed alcohol abuse more frequently and used health care, particularly inpatient treatment, more frequently than those without psychotic features. Within patients with psychotic symptoms, those with MI features showed more clinical severity in terms of a higher likelihood of reporting hallucinations, suicidal attempts and comorbid cannabis dependence. CONCLUSION: Our data provide additional support for both the distinction between BD-I with and without psychotic features as well as the distinction between MI and MC psychotic features. The more severe course of patients with psychotic features, and particularly those with MI psychotic features, highlights the need for thorough psychopathological evaluations to assess the presence of these symptoms to install appropriate treatment.
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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 | 0.000 |
| 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".