Survey of Physiotherapy Practice in Ontario Cardiac Surgery Intensive Care Units
Bibliographic record
Abstract
Purpose: This article describes current physiotherapy practice for critically ill adult patients requiring prolonged stays in critical care (> 3 d) after complicated cardiac surgery in Ontario. Method: We distributed an electronic, self-administered 52-item survey to 35 critical care physiotherapists who treat adult cardiac surgery patients at 11 cardiac surgical sites. Pilot testing and clinical sensibility testing were conducted beforehand. Participants were sent four email reminders. Results: The response rate was 80% (28/35). The median reported number of cardiac surgeries performed per week was 30 (interquartile range [IQR] 10), with a median number of 14.5 (IQR 4) cardiac surgery beds per site. Typical reported caseloads ranged from 6 to 10 patients per day per therapist, and 93% reported that they had initiated physiotherapy with patients once they were clinically stable in the intensive care unit. Of 28 treatments, range of motion exercises (27; 96.4%), airway clearance techniques (26; 92.9%), and sitting at the edge of the bed (25; 89.3%) were the most common. Intra-aortic balloon pump and extracorporeal membrane oxygenation appeared to limit physiotherapy practice. Use of outcome measures was limited. Conclusions: Physiotherapists provide a variety of interventions to critically ill cardiac surgery patients. Further evaluation of the limited use of outcome measures in the cardiac surgical intensive care unit is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".