The Older Persons’ Transitions in Care (OPTIC) study: pilot testing of the transition tracking tool
Bibliographic record
Abstract
BACKGROUND: OPTIC is a mixed method Partnership for Health System Improvement (http://www.cihr-irsc.gc.ca/e/34348.html) study focused on improving care for nursing home (NH) residents who are transferred to and from emergency departments (EDs) via emergency medical services (EMS). In the pilot study we tested feasibility of concurrently collecting individual resident data during transitions across settings using the Transition Tracking Tool (T3). METHODS: The pilot study tracked 54 residents transferred from NHs to one of two EDs in two western Canadian provinces over a three month period. The T3 is an electronic data collection tool developed for this study to record data relevant to describing and determining success of transitions in care. It comprises 800+ data elements including resident characteristics, reasons and precipitating factors for transfer, advance directives, family involvement, healthcare services provided, disposition decisions, and dates/times and timing. RESULTS: Residents were elderly (mean age = 87.1 years) and the majority were female (61.8%). Feasibility of collecting data from multiple sources across two research sites was established. We identified resources and requirements to access and retrieve specific data elements in various settings to manage data collection processes and allocate research staff resources. We present preliminary data from NH, EMS, and ED settings. CONCLUSIONS: While most research in this area has focused on a unidirectional process of patient progression from one care setting to another, this study established feasibility of collecting detailed data from beginning to end of a transition across multiple settings and in multiple directions.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".