Describing and measuring recovery and rehabilitation after critical illness
Bibliographic record
Abstract
PURPOSE OF REVIEW: Rehabilitation is the cornerstone of management of postcritical illness morbidity. Selection of appropriate tools to measure response to rehabilitation therapy is vital to accurately document trajectory of change across the recovery continuum. In the context of physical-based strategies to redress critical illness associated muscle wasting and dysfunction, this review will discuss a framework to guide assessment of physical recovery in the critical illness population, clinimetric measurement properties for instruments and evidence for their implementation, and recent interventional trial data. RECENT FINDINGS: The International Classification of Functioning, Disability and Health (ICF) model is a useful framework to guide selection of outcome measures representing physical function at the level of impairment, activity limitation and participation restriction. Clinimetric data are emerging to support a number of physical function outcome measures in the ICU, albeit further research is required to corroborate tools used beyond ICU discharge. Factors associated with outcome measure selection have contributed to interpreting findings from recent interventional trials of physical rehabilitation. SUMMARY: Determining the future design, conduct and impact of physical therapy interventions for critically ill patients will rely on further development of clinimetrically robust metrics to capture individual patient response spanning the recovery pathway. This approach should be similarly applied to rehabilitation interventions addressing other postintensive care syndrome domains.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".