Predictive variables for poor long‐term physical recovery after intensive care unit stay: An exploratory study
Bibliographic record
Abstract
BACKGROUND: Elucidating factors that influence physical recovery of survivors after an intensive care unit (ICU) stay is paramount in maximizing long-term functional outcomes. We examined potential predictors for poor long-term physical recovery in ICU survivors. METHODS: Based on secondary analysis of a trial of 50 ICU patients who underwent mobilization in the ICU and were followed for one year, linear regression analysis examined the associations of exposure variables (baseline characteristics, severity of illness variables, ICU-related variables, and lengths of ICU and hospital stay), with physical recovery variables (muscle strength, exercise capacity, and self-reported physical function), measured one year after ICU discharge. RESULTS: When the data were adjusted for age, female gender was associated with reduced muscle strength (P = .003), exercise capacity (P < .0001), and self-reported physical function (P = .01). Older age, when adjusted for gender, was associated with reduced exercise capacity (P < .001). After adjusting for gender and age, an association was observed between a lower score on one or two physical recovery variables and exposure variables, specifically, high body mass index, low functional independence, comorbidity and low self-reported physical function at baseline, muscle weakness at ICU discharge, and longer hospital stay. No adjustment was made for cumulative type I error rate due to small number of participants. CONCLUSION: Elucidating risk factors for poor long-term physical recovery after ICU stay, including gender, may be critical if mobilization and exercise are to be prescribed expediently during and after ICU stay, to ensure maximal long-term recovery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".