Does the Ranking Matter? A Retrospective Cohort Study Investigating the Impact of the <i>2018 CANMAT and ISBD Guidelines for the Management of Patients with Bipolar Disorder</i> Treatment Recommendations for Acute Mania on Rehospitalization Rates
Bibliographic record
Abstract
Objective There is limited data about the impact of mood disorders treatment guidelines on clinical outcomes. The objective of this study was to investigate the impact of prescribers’ adherence to the 2018 Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) treatment guidelines recommendations on the readmission rates of patients hospitalized for mania. Method A retrospective cohort of all individuals admitted due to acute mania to Kingston General Hospital, Kingston, ON, from January 2018 to July 2021 was included in this study. Patient variables and data regarding index admission and subsequent hospitalizations were extracted from medical records up to December 31, 2021. Treatment regimens were classified as first-line, second-line, noncompliant, or no treatment. We explored the associations between treatment regimens and the risk of readmissions using univariate, multivariate, and survival analysis. Results We identified 211 hospitalizations related to 165 patients. The mean time-to-readmission was 211.8 days (standard deviation [SD] = 247.1); the 30-day rehospitalization rate was 13.7%, and any rehospitalization rate was 40.3%. Compared to no treatment, only first-line treatments were associated with a statistically significant decreased risk of 30-day readmission (odds ratio [OR] = 0.209; 95% CI, 0.058 to 0.670). The risk of any readmission was reduced by first-line (OR = 0.387; 95% CI, 0.173 to 0.848) and noncompliant regimens (OR = 0.414; 95% CI, 0.174 to 0.982) compared to no treatment. On survival analysis, no treatment group was associated with shorter time-to-readmission (log-rank test, p = 0.014) and increased risk of readmission (hazard ratio = 2.27; 95% CI, 1.30 to 3.96) when compared to first-line medications. Conclusions Treatment with first-line medications was associated with lower 30-day rehospitalization rates and longer time-to-readmission. Physicians’ adherence to treatments with higher-ranked evidence for efficacy, safety, and tolerability may improve bipolar disorder outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".