Joint contractures in the intensive care unit: association with resource utilization and ambulatory status at discharge
Bibliographic record
Abstract
PURPOSE: The objectives of our study were (1) to explore the link between joint contractures acquired in the ICU and the ambulatory status of patients at discharge home, to determine (2) when and how many patients received physiotherapy services in ICU and on the hospital ward, and (3) the differences in the use of hospital resources in the presence or absence of joint contractures. METHOD: Data on ICU joint contractures were extracted from an existing contracture database containing information on 155 Canadian patients with a tertiary ICU stay of 14 days or more. RESULTS: Of 155 patients, 115 (74.2%) received a range of motion assessment in the ICU. The assessment took place a median of 7 days (IQR 0-36) after ICU admission. Significantly fewer patients with joint contractures than without joint contractures were mobilized on the hospital ward (21/38 [55.3%] vs. 27/34 [79.4%], P = 0.03). At discharge home, more patients with joint contractures had a low ambulatory status (38 [64.4%]) compared with patients without joint contractures (26 [51.0%]; P = 0.002). CONCLUSION: The median delay of 7 days before musculoskeletal assessment in the ICU together with failure to assess 26% of patients may have allowed the development of contractures, which affected the patients' ambulatory status at discharge from hospital.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".