Technology to enhance physical rehabilitation of critically ill patients
Bibliographic record
Abstract
BACKGROUND: Neuromuscular complications after critical illness are common and can be severe and persistent. To ameliorate complications, there is growing interest in starting physical medicine and rehabilitation therapy immediately after physiologic stabilization. The introduction of physical medicine and rehabilitation-related technology into the intensive care unit may help facilitate delivery of this therapy. DISCUSSION: Neuromuscular electrical stimulation therapy creates passive contraction of muscles through low-voltage electrical impulses delivered through skin electrodes placed over target muscles. Although neuromuscular electrical stimulation has not been studied in patients with acute critical illness, published guidelines based on available evidence suggest that neuromuscular electrical stimulation may be considered in intensive care unit patients who are at high risk of developing muscle weakness. Bedside cycle ergometry can provide range of motion and muscle strength training for intensive care unit patients who are either sedated or awake, and may help preserve muscle architecture and improve strength and function. Finally, custom-designed technological aids to assist with ambulating mechanically ventilated patients may reduce the human resource requirements and improve the safety and effectiveness of early mobilization in the intensive care unit. CONCLUSION: Physical medicine and rehabilitation-related technologies may play an important role in preventing and treating intensive care unit-acquired neuromuscular complications. Future studies are needed to evaluate their efficacy in intensive care unit 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".