Very Early Rehabilitation or Intensive Telemetry after Stroke: A Pilot Randomised Trial
Bibliographic record
Abstract
BACKGROUND: Stroke patients are more likely to make a good recovery if they receive care in a well-organised stroke unit. However, there are uncertainties about how best to provide such care. We studied 2 key aspects of early stroke unit care: early active mobilisation (EM) and automated monitoring (AM) for physiological complications such as hypoxia. METHODS: This was an observer-blinded, factorial (2 x 2) pilot randomised controlled trial recruiting stroke patients within 36 h of symptom onset. The patients were randomised to 1 of 4 nurse-led treatment protocols: (a) standard stroke unit care, (b) EM, (c) AM or (d) combined EM and AM. The primary outcome was the Rankin score at 3 months. We also report the data on feasibility and safety. RESULTS: We randomised 32 patients (mean age = 65 years; mean baseline modified NIH score = 6). On unadjusted comparisons, the EM patients were significantly (p < 0.05) more likely to mobilise very early (within 1 h of randomisation) and to achieve walking by day 5 and were less likely to develop complications of immobility. The AM group was significantly (p < 0.05) more likely to have pre-defined physiological complication events detected. All these associations remained, but were less statistically significant, after correcting for age, baseline NIH score and co-interventions. There were no significant safety concerns. DISCUSSION: We have demonstrated the feasibility of implementing EM and AM for physiological complications in a randomised controlled trial. Larger trials are warranted to determine whether these interventions have clinical benefits.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".