224Diagnosing Dementia in the Rehabilitation Setting: Prevalence, Patient Characteristics and Rehabilitation Outcomes
Bibliographic record
Abstract
We aimed to examine the number of patients diagnosed with dementia during their rehabilitation admission. Dementia can have varying effects on a person’s participation in rehabilitation impacting on rehabilitation potential. It is important to disclose a dementia diagnosis to patients and their families to enable future planning as well as availing of support services. We retrospectively examined all admissions to the Acute Rehabilitation Unit (ARU) over a 12 month period using the online database Bluespiers. We reviewed all patients’ records with a diagnosis of dementia. In this cohort additional data including demographics, medications, discharge destination, length of stay and Barthel index pre and post rehab were recorded. 437 patients were admitted to ARU over the study period. 91 (20.8%) had a diagnosis of dementia; 13.2% had the diagnosis prior to admission, 92% of whom were on medications. Those with dementia had a mean age (SD) of 83.69 (5.65) years. Sixty-nine (76.7%) patients had neuro-imaging at some point. The median (IQR) Barthel Index preadmission was 11/20 (2–20), postadmission 13/20 (7–19). The mean (SD) Montreal Cognitive Assessment Score was 14.40 (4.67) with median (IQR) of 14 (6–24). 79.1% (72) of patients returned home, 0.02% (2) back to referring hospital unwell and 18.7% (17) to long-term care. Eighty-one (94.2%) were on anti-dementia medications on discharge; 59.6% (53) on memantine, 11.1% (10) on donepezil, 1.1% (1) on rivastigmine and 5.5% (5) on galantamine. Only 54.9% (50) patients had dementia diagnosis documented on discharge letters to primary care. 1 in 5 patients attending the ARU had a diagnosis of dementia. Rehab potential was good in this population with most patients returning to their own homes. Communication of this diagnosis to patients GP in discharge letters was poor. We hope to put in place a protocol to standardise and improve how we assess and manage patients with dementia in the ARU and re-audit our practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".