Prevalence of Dementia in a Geriatric Palliative Care Unit
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of dementia in a palliative care unit (PCU) and to determine whether there is a difference between length of stay (LOS) and Palliative Performance Scale (PPS) score in individuals admitted with a primary diagnosis of dementia compared to individuals admitted with other noncancer and cancer diagnoses. DESIGN: Descriptive retrospective chart review. SETTING: Geriatric PCU in an academic community geriatric hospital. PARTICIPANTS: All individuals admitted to the Baycrest Health Sciences PCU from January 1, 2014, to September 1, 2016. MEASUREMENTS: Individuals with an admission diagnosis of cancer, noncancer, and dementia and their corresponding PPS scores were identified. Data were analyzed using descriptive statistics. RESULTS: A total of 780 patients were admitted to the PCU during the study period: 32 (4.1%) individuals had advanced dementia, 121 (15.5%) had a noncancer diagnosis, and 627 (80.4%) had cancer as the primary reason for admission. In the cancer and noncancer groups, 113 patients had a comorbid dementia diagnosis. The mean admission PPS score in patients with cancer was 36%, noncancer was 32.6%, and dementia was 23.8% ( P < .001). Mean LOS in patients with cancer was 32 days, noncancer patients was 34.3 days, and patients with advanced dementia was 33.3 days ( P = .90). CONCLUSIONS: Individuals with an admission diagnosis of advanced dementia had a lower mean PPS score than individuals admitted with other noncancer and cancer diagnoses. There was no difference in the mean LOS between the 3 groups. Individuals with an admission diagnosis of advanced dementia should not be refused admission because of fear of outliving their prognosis.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".