Early Mobilization Interventions in the Intensive Care Unit: Ongoing and Unpublished Randomized Trials
Bibliographic record
Abstract
Background. Critical care societies recommend early mobilization (EM) as standard practice in the intensive care unit (ICU) setting. However, there is limited randomized controlled trial (RCT) evidence supporting EM’s effectiveness. Our objective was to identify ongoing or completed RCTs assessing EM’s effectiveness in the ICU. Method. We searched ClinicalTrials.gov and the Australian New Zealand Clinical Trials Registry for ongoing or completed but not published RCTs in an ICU setting with objective outcome measures. Results. There were 14 RCTs included in the analysis. All studies were in the general or mixed ICU setting ( N=14 ). Half of the studies ( N=7 ) were small RCTs (<100 projected participants) and half ( N=7 ) were medium-sized RCTs (100–999 participants). Inclusion criteria included mechanical ventilation use or expected use ( N=13 ) and prehospital functional status ( N=7 ). Primary EM interventions were standard physiotherapist-based activities ( N=4 ), cycling ( N=9 ), and electrical muscle stimulation ( N=1 ). Only one study involved nurse-led EM. The most common assessment tool was the 6-minute walk test ( N=6 ). Primary outcome measures were physiological ( N=3 ), clinical ( N=3 ), patient-centered ( N=7 ), and healthcare resource use ( N=1 ). Most studies ( N=8 ) involved post-ICU follow-up measures up to 1-year posthospitalization. There were no studies targeting older adults or people with acute cardiac disease. Conclusion. Identified studies will further the evidence base for EM’s effectiveness. There is a need for studies looking at specific patient populations that may benefit from EM, such as older adults and cardiac patients, as well as for novel EM delivery strategies, such as nurse-led EM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.113 | 0.258 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".