Characteristics Associated with Inpatient Versus Outpatient Status in Older Adults with Bipolar Disorder
Bibliographic record
Abstract
OBJECTIVES: This is an exploratory analysis of ambulatory and inpatient services utilization by older persons with type I bipolar disorder experiencing elevated mood. The association between type of treatment setting and the person's characteristics is explored within a framework that focuses upon predisposing, enhancing, and need characteristics. METHOD: Baseline assessments were conducted with the first 51 inpatients and 49 outpatients 60 years of age and older, meeting criteria for type I bipolar disorder, manic, hypomanic, or mixed episode enrolled in the geriatric bipolar disorder study (GERI-BD) study. We compared participants recruited from inpatient versus outpatient settings in regard to the patients' predisposing, enabling, and need characteristics. RESULTS: Being treated in an inpatient rather than an outpatient setting was associated with the predisposing characteristic of being non-Hispanic caucasian (odds ratio [OR]: 0.1; P = .005) and past history of treatment with first-generation antipsychotics (OR: 6.5; P < .001), and the need characteristic reflected in having psychotic symptoms present in the current episode (OR: 126.08; P < .001). CONCLUSION: Ethnicity, past pharmacologic treatment, and current symptom severity are closely associated with treatment in inpatient settings. Clinicians and researchers should investigate whether closer monitoring of persons with well-validated predisposing and need characteristics can lead to their being treated in less costly but equally effective ambulatory rather than inpatient settings.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".