Surviving critical illness: what is next? An expert consensus statement on physical rehabilitation after hospital discharge
Bibliographic record
Abstract
BACKGROUND: The study objective was to obtain consensus on physical therapy (PT) in the rehabilitation of critical illness survivors after hospital discharge. Research questions were: what are PT goals, what are recommended measurement tools, and what constitutes an optimal PT intervention for survivors of critical illness? METHODS: A Delphi consensus study was conducted. Panelists were included based on relevant fields of expertise, years of clinical experience, and publication record. A literature review determined five themes, forming the basis for Delphi round one, which was aimed at generating ideas. Statements were drafted and ranked on a 5-point Likert scale in two additional rounds with the objective to reach consensus. Results were expressed as median and semi-interquartile range, with the consensus threshold set at ≤0.5. RESULTS: Ten internationally established researchers and clinicians participated in this Delphi panel, with a response rate of 80 %, 100 %, and 100 % across three rounds. Consensus was reached on 88.5 % of the statements, resulting in a framework for PT after hospital discharge. Essential handover information should include information on 15 parameters. A core set of outcomes should test exercise capacity, skeletal muscle strength, function in activities of daily living, mobility, quality of life, and pain. PT interventions should include functional exercises, circuit and endurance training, strengthening exercises for limb and respiratory muscles, education on recovery, and a nutritional component. Screening tools to identify impairments in other health domains and referral to specialists are proposed. CONCLUSIONS: A consensus-based framework for optimal PT after hospital discharge is proposed. Future research should focus on feasibility testing of this framework, developing risk stratification tools and validating core outcome measures for ICU survivors.
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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 | 0.249 | 0.202 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".