Prognosis of cardiac arrest in home care clients and nursing home residents: A population-level retrospective cohort study
Bibliographic record
Abstract
Aim: To evaluate the prognosis of 30-day survival post-cardiac arrest among patients receiving home care and nursing home residents. Methods: We conducted a population-level retrospective cohort study of community-dwelling adults (≥18 years) who received cardiac arrest care at a hospital in Ontario, Canada, between 2006 to 2018. We linked population-based health datasets using the Home Care Dataset to identify patients receiving home care and the Continuing Care Reporting System to identify nursing home residents. We included both out-of-hospital and in-hospital cardiac arrests. We determined unadjusted and adjusted associations using logistic regression after adjusting for age and sex. We converted relative measures to absolute risks. Results: Our cohort contained 86,836 individuals. Most arrests (55.5 %) occurred out-of-hospital, with 9,316 patients enrolled in home care and 2,394 residing in a nursing home. When compared to those receiving no support services, the likelihood of survival to 30-days was lower for those receiving home care (RD = -6.5; 95 %CI = -7.5 - -5.0), with similar results found within sub-groups of out-of-hospital (RD = -6.7; 95 %CI = -7.6 - -5.7) and in-hospital arrests (RD = -8.7; 95 %CI = -10.6 - -7.3). The likelihood of 30-day survival was lower for nursing home residents (RD = -7.2; 95 %CI = -9.3 - -5.3) with similar results found within sub-groups of out-of-hospital (RD = -8.6; 95 %CI = -10.6 - -5.7) and in-hospital arrests (RD = -5.0; 95 %CI = -7.8 - -2.1). Conclusion: Patients receiving home care and nursing home residents had worse overall prognoses of survival post-cardiac arrest compared to those receiving no pre-arrest support, highlighting two medically-complex groups likely to benefit from advance care planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".