Associations of medical comorbidity, psychosis, pain, and capacity with psychiatric hospital length of stay in geriatric inpatients with and without dementia
Bibliographic record
Abstract
BACKGROUND: Geriatric psychiatry hospital beds are a limited resource. Our aim was to determine predictors of hospital length of stay (LOS) for geriatric patients with dementia admitted to inpatient psychiatric beds. METHODS: Admission and discharge data from a large urban mental health center, from 2005 to 2010 inclusive, were retrospectively analyzed. Using the resident assessment instrument - mental health (RAI-MH), an assessment that is used to collect demographic and clinical information within 72 hours of hospital admission, 169 geriatric patients with dementia were compared with 308 geriatric patients without dementia. Predictors of hospital LOS were determined using a series of general linear models. RESULTS: A diagnosis of dementia did not predict a longer LOS in this geriatric psychiatry inpatient population. The presence of multiple medical co-morbidities had an inverse relationship to length of hospital LOS - a greater number of co-morbidities predicted a shorter hospital LOS in the group of geriatric patients who had dementia compared to the without dementia study group. The presence of incapacity and positive psychotic symptoms predicted longer hospital LOS, irrespective of admission group (patients with dementia compared with those without). Conversely, pain on admission predicted shorter hospital LOS. CONCLUSIONS: Specific clinical characteristics generally determined at the time of admission are predictive of hospital LOS in geriatric psychiatry inpatients. Addressing these 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".