A 12-month prospective study on the time to hospitalization and clinical management of a cohort of bipolar type I and schizoaffective bipolar patients
Bibliographic record
Abstract
BACKGROUND: Schizoaffective disorder, bipolar type (SAD) and bipolar disorder I (BD) present a large clinical overlap. In a 1-year follow-up, we aimed to evaluate days to hospitalization (DTH) and predictors of relapse in a SAD-BD cohort of patients. METHODS: A 1-year, prospective, naturalistic cohort study considering DTH as primary outcome and incidence of direct and indirect measures of psychopathological compensation as secondary outcomes. Kaplan-Meyer survival analysis with Log-rank Mantel-Cox test compared BD/SAD subgroups as to DTH. After bivariate analyses, Cox regression was performed to assess covariates possibly associated with DTH in diagnostic subgroups. RESULTS: Of 836 screened patients, 437 were finally included (SAD = 105; BD = 332). Relapse rates in the SAD sample was n = 26 (24.8%) vs. n = 41 (12.3%) in the BD sample (p = 0.002). Mean ± SD DTH were 312.16 ± 10.6 (SAD) vs. 337.62 ± 4.4 (BD) days (p = 0.002). Patients with relapses showed more frequent suicide acts, violent behaviors, and changes in pharmacological treatments (all p < 0.0005) in comparison to patients without relapse. Patients without relapses had significantly higher mean number of treatments at T0 (p = 0.010). Cox regression model relating the association between diagnosis and DTH revealed that BD had higher rates of suicide attempts (HR = 13.0, 95%CI = 4.0-42.0, p < 0.0005), whereas SAD had higher rates of violent behavior during psychotic episodes (HR = 12.0, 95%CI = .3.3-43.5, p > 0.0005). CONCLUSIONS: SAD patients relapse earlier with higher hospitalization rates and violent behavior during psychotic episodes whereas bipolar patients have more suicide attempts. Psychiatric/psychological follow-up visits may delay hospitalizations by closely monitoring symptoms of self- and hetero-aggression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".