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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it