Trajectoires des patients âgés en fin de soins actifs
Bibliographic record
Abstract
INTRODUCTION: To determine the palliative care pathways of older patients in Sherbrooke, Qc by examining their transfers to other facilities. METHODS: This analysis was conducted by linking 3 databases: emergency department, hospitalizations and nursing homes. The study period ranged from January 2011 to December 2015. SPSS was used for statistical analysis. The study only included palliative care patients. RESULTS: 25% of patients waited less than 7 days for transfer, and 74% waited less than 3 weeks. 64.9% of patients were transferred to a long-term facility for dependent adults (LTF), 15.2% returned home or were transferred to private accommodation, and 15.9% were transferred to an intermediate care facility. One-half of patients subsequently changed facility, mainly those in homes or intermediate care. Palliative care patient bed occupation rates represented 1% of available bed-days and less than 2% of total beds for 86.4% of days. Only 12% of patients returned to hospital within 90 days after discharge. CONCLUSION: The number of beds occupied by palliative care patients does not seem to disrupt the hospital capacity. The majority of the palliative care patients were well managed, as reflected by the low readmission rate. Our results indicate good management of transfers and an adequate supply of long-term care facilities and home services.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".