The Rehabilitation Tracking/Reporting Grid : a Tool Who Connects Early-intern Unit and Communauty Rehabilitation For Continuus Care of Stoke Patients
Bibliographic record
Abstract
In many regions in Quebec, early-intern units and communauty rehabilitation services are provided to stroke patients, by different teams in different settings (hospitals, hospitalu2019s external facilities, external clinics) who have their own admission process. This situation can lead to some additionnal delay or breakdown in services during the transition phases. Because the registry information systems for patients used in hospitals and rehabilitation settings are different, no one is aware of what might go wrong in transition. In the Laurentian region, heads of many rehabilitation programs have set up a simple tool (Excel program) enabling us to track and report patients, who had suffered a stoke, from their hospitalisation to their release to external rehabilitation care. The rehabilitation tracking/reporting grid (Rehab TRG) is designed to be easely filled with information regarding patientu2019s course of care and helping caregivers to identify and keep track of patients. The informations compiled in the grid are also easy to access in day to day care organisation and allows easy calculation of different deadlines to meet. An annual report made from the collected informations allows readjustments of clinical rehabilitation path. The Rehab TRG keeps institutions related to each other by improving access and continuity care for patients who are recovering from a stroke. Great benefits have been gained with the use of what became a precious information system permitting us to be connected in a climate of transparency.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.028 |
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".