Assessing Decision-Making Tools for Meaningful Discussions About Transfer Decisions From Long-Term Care to Hospital: A Scoping Review
Bibliographic record
Abstract
The decision-making process for transferring long-term care (LTC) residents to acute care hospitals is complex. These transfers can carry significant health risks, such as loss of function, morbidity, and mortality. Decision-making tools have emerged to guide these transfers, offering structured frameworks for informed discussions. Despite their benefits, their application remains inconsistent across LTC settings. MEDLINE, Embase, and CENTRAL were searched, following PRISMA-Scr guidelines. Studies evaluating decision support tools for LTC-to-hospital transfers were included. Of 1,383 studies identified, 15 studies involving 50,175 patients were included. Tools were categorized into five intervention types: educational booklets, decision aid videos, multidisciplinary programs, advance directive programs, and checklists. Most studies reported reduced hospitalization rates and improved communication, but variability in tool types highlighted their fragmented application. This scoping review summarizes reported outcomes and highlights gaps in the application and evaluation of decision-making tools for LTC-to-hospital transfers, emphasizing the need for more standardized and culturally sensitive interventions.
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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 | 0.092 | 0.283 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".