Examining Do-Not-Resuscitate Orders among Newly Admitted Residents of Long-term Care Facilities
Why this work is in the frame
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Bibliographic record
Abstract
Do-not-resuscitate (DNR) orders are an important part of advance directives. To date, little is known about DNR orders in Ontario's long-term care (LTC) facilities. The Canadian Institute for Health Information (CIHI) stated that in between 2011 and 2012, there were more than 32,000 discharges from Ontario's LTC facilities, 44% of which resulted from death. This study examined DNR orders in LTC homes in Ontario. The sample includes all LTC residents receiving care between 2010 and 2012. Data provided by the CIHI were collected using the Canadian version of the Resident Assessment Instrument. The data included administrative assessments on health of 112,746 residents. The average age of LTC residents in this study was 84.5 years, and about 70% were female residents. Results showed that residents admitted from home were less likely to have a DNR order on file during assessment and three months later. Residents whose families were responsible for care were more likely to have DNR orders when admitted, but this effect was not found at three-month follow-up. Residents who were in end-stage diseases were more likely to have completed DNR orders upon admission to LTC facilities. The presence of a health condition (eg frailty, depression, heart condition, pulmonary or psychiatric condition) increased the likelihood of residents having DNR orders when admitted to LTC facilities. Residents whose conditions were deteriorating were more likely to have completed DNR orders before the three-month follow-up. In conclusion, this study represents an important step in identifying issues related to DNR orders in LTC facilities. The factors that influence whether residents have DNR orders on file upon admission depend on the presence of family members, whether the residents are designated as end-of-life cases (six months or less), older age, and health. Discussions about resuscitation are an important part of care plans.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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