Optimizing<u>MO</u>bility for critically ill pati<u>E</u>nts undergoing Continuous Renal Replacement Therapy (MOvE CRRT): An audit of mobility interventions in the intensive care unit
Bibliographic record
Abstract
BACKGROUND: CRRT is common in the ICU. This intervention has been shown to contribute to reduced mobilization due to fear of adverse events. This study sought to evaluate the degree of mobilization in patients receiving CRRT and to develop a procedure checklist to enhance mobilization in these patients.METHODS: A retrospective observational matched cohort audit of adult patients admitted to the General Systems ICU at the University of Alberta Hospital from April 1, 2015, and April 1, 2017 was conducted. A total of 50 CRRT patients were matched to 37 critically ill patients and their mobilization events compared. Data was analyzed descriptively. A protocolized mobilization procedure checklist was subsequently developed.RESULTS: Higher levels of mobility were achieved in patients not receiving CRRT. The highest level of mobility in CRRT patients was ambulation in 1 (2%), active mobilization in 17 (34%), passive mobilization in 13 (26%) and none in 19 (38%); whereas, in controls, the highest level of mobility was ambulatory in 22 (59%), active in 10 (27%), passive in 2 (5%) and none in 3 (8%). Four (8%) of the CRRT patients had a PT program delay attributed to CRRT. Adverse events were uncommon and unrelated to CRRT, occurring in 1 (2%) of CRRT patients and in 3 (8%) control patients. No critical adverse events occurred, and no CRRT was delayed or paused. Alarms limited or postponed treatment in 7 (14%) patients.CONCLUSIONS: Mobilization while on CRRT is feasible and safe. It is conducted less frequently and to a lesser degree when compared to similarly acute patients not receiving CRRT. A procedure checklist has been developed to improve mobilization while on CRRT that can be safely implemented in critically ill patients.
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 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 | 0.001 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".