Effects of mobility dose on discharge disposition in critically ill stroke patients
Bibliographic record
Abstract
BACKGROUND: Mobilization in the intensive care unit (ICU) has the potential to improve patient outcomes following acute stroke. The optimal duration and intensity of mobilization for patients with hemorrhagic or ischemic stroke in the ICU remain unclear. OBJECTIVE: To assess the effect of mobilization dose in the ICU on adverse discharge disposition in patients after stroke. DESIGN: This is an international, prospective, observational cohort study of critically ill stroke patients (November 2017-September 2019). Duration and intensity of mobilization was quantified daily by the mobilization quantification score (MQS). SETTING: Patients requiring ICU-level care were enrolled within 48 hours of admission at four separate academic medical centers (two in Europe, two in the United States). PARTICIPANTS: Participants included individuals (>18 years old) admitted to an ICU within 48 hours of ischemic or hemorrhagic stroke onset who were functionally independent at baseline. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: The primary outcome was adverse discharge disposition. RESULTS: Of the patients screened, 163 were eligible for inclusion in the study. One patient was subsequently excluded due to insufficient data collection (n = 162). The dose of mobilization varied greatly between centers and patients, which could not be explained by patients' comorbidities or disease severity. High dose of mobilization (mean MQS > 7.3) was associated with a lower likelihood of adverse discharge (adjusted odds ratio, [aOR]: 0.14; 95% confidence interval [CI]: 0.06-0.31; p < .01). CONCLUSION: The increased use of mobilization acutely in the ICU setting may improve patient outcomes.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".