Real-time tracking of functional performance using accelerometers on the acute-stroke unit: proof-of-concept study
Bibliographic record
Abstract
Background: Acute stroke care pathways are increasingly implemented to improve integration of best-practices, but evidence for impact on functional outcomes is not strong. Elements missing from care-pathways are those directly targeting improvement in function: sit-to-stand and time spent walking. The Stroke Team uses care-pathways to track functional capacity, what the patient can and cannot do, but performance on these key outcomes is difficult to track as the patient is observed by multiple people throughout the day. The purpose of this study is to demonstrate the feasibility and added-value of real-time tracking of patients’ mobility. Methods: A chart review was carried out to identify the extent to which functional capacity and performance is tracked routinely by the Stroke Team. Ethical approval was gained for routine use of accelerometers to be affixed to the unaffected thigh. Results: Swallowing, bladder control, toileting, and feeding were consistently tracked for ~90% of patients. Bed-mobility and capacity to transfer rarely tracked (<12%). Capacity for walking and sit-to-stand was noted but never frequency (performance). Conclusion: Our proof-of concept study will test 30 patients over the next 2 months and link real time performance on transitions and walking to stroke severity and outcome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".