Predicting Hospital Length of Stay for Geriatric Patients with Mood Disorders
Bibliographic record
Abstract
OBJECTIVE: To determine predictors of hospital length of stay (LOS) for adult and geriatric patients with mood disorders admitted to inpatient psychiatric beds. METHOD: Admission and discharge data from a large urban mental health centre, from 2005 to 2010 inclusive, were retrospectively analyzed. Using the Resident Assessment Instrument-Mental Health, an assessment that is used to collect demographic and clinical information within 72 hours of hospital admission, 199 geriatric mood disorder admissions were compared with 570 adult mood disorder admissions. Predictors of hospital LOS were determined using a series of general linear models. RESULTS: Living alone, number of recent psychiatric admissions, involuntary admission, and close or constant observation level predict longer hospital LOS in geriatric, but not in adult mood disorder, patients. Conversely, pain on admission predicts shorter hospital LOS in geriatric, but not among adult, mood disorder patients. Predictors of longer hospital LOS, irrespective of admission group (adult, compared with geriatric), include incapacity, negative symptoms, and increased dependence for instrumental activities of daily living. CONCLUSIONS: Addressing these predictive factors early on during admission and in the community may result in shorter hospital LOS and more optimal use of resources.
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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 | 0.004 |
| 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 | 0.001 |
| 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".