Safety and Feasibility of a Protocolized Approach to In-Bed Cycling Exercise in the Intensive Care Unit: Quality Improvement Project
Bibliographic record
Abstract
BACKGROUND: In-bed, supine cycle ergometry as a part of early rehabilitation in the intensive care unit (ICU) appears to be safe, feasible, and beneficial, but no standardized protocol exists. A standardized protocol may help guide use of cycle ergometry in the ICU. OBJECTIVE: This study investigated whether a standardized protocol for in-bed cycling is safe and feasible, results in cycling for a longer duration, and achieves a higher resistance. DESIGN: A quality improvement (QI) project was conducted. METHODS: A 35-minute in-bed cycling protocol was implemented in a single medical intensive care unit (MICU) over a 7-month quality improvement (QI) period compared to pre-existing, prospectively collected data from an 18-month pre-QI period. RESULTS: One hundred and six MICU patients received 260 cycling sessions in the QI period vs. 178 MICU patients receiving 498 sessions in the pre-QI period. The protocol was used in 249 (96%) of cycling sessions. The QI group cycled for longer median (IQR) duration (35 [25-35] vs. 25 [18-30] minutes, P < .001) and more frequently achieved a resistance level greater than gear 0 (47% vs. 17% of sessions, P < .001). There were 4 (1.5%) transient physiologic abnormalities during the QI period, and 1 (0.2%) during the pre-QI period ( P = .031). LIMITATIONS: Patient outcomes were not evaluated to understand if the protocol has clinical benefits. CONCLUSIONS: Use of a protocolized approach for in-bed cycling appears safe and feasible, results in cycling for longer duration, and achieved higher resistance.
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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 | 0.133 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".