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.
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 | 0.001 | 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 | 0.000 | 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".