FACTORS ASSOCIATED WITH MORTALITY AMONG LONG-TERM CARE RESIDENTS TRANSITIONING TO AND FROM EMERGENCY DEPARTMENTS
Bibliographic record
Abstract
Abstract Studies examining risk of death during acute care transitions have highlighted potential predictors of death during transition. However, they have not closely examined the relationships and directional effects of organizational context, care processes, resident demographics and health conditions on death during transition. By employing structural equation modeling, we aimed to 1) identify predictive factors for residents who died during transitions from long term care (LTC) to emergency departments (EDs) and back; 2) examine relationships between identified organizational, process and resident factors with resident death during these transitions; and 3) identify areas for further investigation and improvement in practice. We tracked every resident transfer from 38 participating LTC facilities to two included EDs in two Western Canadian provinces from July 2011 to July 2012. Overall, 524 residents were involved in 637 transfers of whom 63 residents (12%) died during the transition. Sustained dyspnea (in both LTC and the ED), sustained change in level of consciousness (LOC) and severity measured by triage score were direct and significant predictors of resident death during transition. The model fit the data, (x2 = 83.77, df = 64, p = 0.049) and explained 15% variance in resident death. Dyspnea and change in LOC in both LTC and ED needs to be recognized regardless of primary reason for transfer. More research is needed to determine the specific influences of LTC ownership models, family involvement in decision-making, LTC staff decision-making on resident death during transition, and interventions to prevent pre-death transfers.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".