Alternate Level of Care Patients in Hospitals: What Does Dementia Have To Do With This?
Bibliographic record
Abstract
BACKGROUND: Patients in acute care hospitals no longer in need of acute care are called Alternate Level of Care (ALC) patients. This is growing and common all across Canada. A better understanding of this patient population would help to address this problem. METHODS: A chart review was conducted in two hospitals in New Brunswick. All patients designated as ALC on July 1, 2009 had their charts reviewed. RESULTS: Thirty-three per cent of the hospital beds were occupied with ALC patients; 63% had a diagnosis of dementia. The mean length of stay was 379.6 days. Eighty-six per cent were awaiting a long-term care bed in the community. Most patients experienced functional decline during their hospitalization. One year prior to admission, 61% had not been admitted to hospital and 59.2% had had at least one visit to the emergency room. CONCLUSIONS: The majority of the ALC patients in hospital have a diagnosis of dementia and have been waiting in hospital for over one year for a long-term care bed in the community. Many participants were recipients of maximum home care in the community, suggesting home maker services alone may not be adequate for some community-dwelling older adults. Early diagnosis of dementia, coupled with appropriate care in the community, may help to curtail the number of patients with dementia who end up in hospital as ALC patients.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".