The development of a clinical management algorithm for early physical activity and mobilization of critically ill patients: synthesis of evidence and expert opinion and its translation into practice
Bibliographic record
Abstract
OBJECTIVE: To facilitate knowledge synthesis and implementation of evidence supporting early physical activity and mobilization of adult patients in the intensive care unit and its translation into practice, we developed an evidence-based clinical management algorithm. METHODS: Twenty-eight draft algorithm statements extracted from the extant literature by the primary research team were verified and rated by scientist clinicians (n = 7) in an electronic three round Delphi process. Algorithm statements which reached a priori defined consensus - semi-interquartile range <0.5 - were collated into the algorithm. RESULTS: The draft algorithm statements were edited and six additional statements were formulated. The 34 statements related to assessment and treatment were grouped into three categories. Category A included statements for unconscious critically ill patients; Category B included statements for stable and cooperative critically ill patients, and Category C included statements related to stable patients with prolonged critical illness. While panellists reached consensus on the ratings of 94% (32/34) of the algorithm statements, only 50% (17/34) of the statements were rated essential. CONCLUSION: The evidence-based clinical management algorithm developed through an established Delphi process of consensus by an international inter-professional panel provides the clinician with a synthesis of current evidence and clinical expert opinion. This framework can be used to facilitate clinical decision making within the context of a given patient. The next step is to determine the clinical utility of this working algorithm.
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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 | 0.227 | 0.303 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".