Death in Long-term Care: A Brief Report Examining Factors Associated with Death within 31 Days of Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: The ability to estimate prognosis using administrative data has already been established. Research indicates that residents newly admitted to long-term care are at a higher risk of mortality. Studies have also examined mortality within 90 days or a year. Focusing on 31 days from assessment was important because it appears to be clinically useful for care planning in end-of-life; whereby, greater utility may come from identifying residents who are at risk of death within a shorter time frame so that advance care planning can occur. PURPOSE: To examine risk of mortality within 31 days of assessment among long-term care residents using administrative health data. METHODS: Administrative data were used to examine risk of mortality within 31 days of assessment among all long-term care residents in Ontario over a 12-month period. Data were provided by the Canadian Institute for Health Information using the Continuing Care Reporting System (CCRS), Discharge Abstract Database (DAD), and the National Ambulatory Care Reporting System (NACRS). RESULTS: A number of diagnoses and health conditions predict death within 31 days. Diagnoses that hold an increased risk of mortality include pulmonary disease, diagnosis of cancer, and heart disease. Health conditions that lead to an increased likelihood of death include weight loss, dehydration, and shortness of breath. The presence of a fall within the last 30 days was also related to a higher risk of mortality. DISCUSSION: Long-term care residents who lose weight, have persistent problems with hydration, and suffer from shortness of breath are at particular risk of death. The presence of advanced directives also predicts death within 31 days of assessment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it