Abstract 13167: Early Mobilization in Postoperative Cardiac Surgical Patients
Bibliographic record
Abstract
Introduction: Early mobilization (EM) is recommended by cardiac surgical societies. However, the optimal method of EM delivery has yet to be determined. Our objective was to assess whether a bedside nurse-driven EM strategy is safe and associated with improved outcomes following cardiac surgery. Methods: Consecutive post-cardiac surgery patients in a cardiovascular intensive care unit (CVICU) at an academic tertiary care centre from 2017 to 2019 prior to and after EM program implementation were reviewed. Postoperative cardiac surgery patients were initially managed in a general ICU and transferred to the CVICU when hemodynamic stability was achieved, typically postoperative day 1 or 2. Functional status was assessed by the nurse on CVICU admission using the Level of Function (LOF) Mobility Scale, which ranges from LOF 0 (bed immobile) to LOF 5 (walks > 50 feet). The nurse uses the LOF score to guide twice-daily level-specific mobility activities. The primary outcome was hospital length of stay. Results: There were 504 patients included in the study (preintervention, N=329; Intervention, N=175). There was no difference in age, sex or comorbid illness between the groups (Table). The LOF was 4.7 ± 0.5 prior to surgery, 3.4 ± 1.1 on CVICU admission, and 4.3 ± 0.6 on CVICU discharge in patients undergoing EM. Patients were mobilized during nearly all mobilization opportunities (98.7%; 685/694). Adverse events were rare (0.4%; 8 events/1901 mobilization activities), minor and transient. There was no difference is postoperative hospital length of stay, in-hospital mortality, discharge home or 30-day hospital re-admission (all P>0.05). Conclusion: A nurse-driven EM program was safe and associated with improvement in functional status in postoperative cardiac surgery patients. The EM program was not associated with improved short-term outcomes. Further studies are needed to understand optimal delivery of EM in cardiac surgical patients.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".