Strategies to achieve adherence to prone positioning in awake COVID-19 patients with highflow nasal oxygen. A case series
Bibliographic record
Abstract
Introduction The use of high-flow nasal oxygen (HFNO) is a simple method that can reduce intubation in patients with hypoxemic acute respiratory failure (ARF). Early and prolonged prone position has demonstrated benefits on mortality in mechanically ventilated patients and on intubation in awake patients with ARF. However, strategies to achieve adherence to awake prone positioning (APP) have not been previously described. Case and outcomes We present six patients with ARF due to COVID-19 treated with HFNO and APP. The median (p25--75) of PaFiO~2~ upon admission was 121 (112--175). The average duration of APP on the first day was 16 h (SD 5 h). Duration (median p25--75) in APP for the following 20 days was 13 (10--18) h/day. Several strategies such as the presence of a health care team, recreational activities, adaptation of the circadian rhythm, oral nutritional support, and analgesics were used to improve prone tolerance. None of the patients suffered from delirium, all were ambulating on discharge from the ICU and none require intubation. Conclusion The case series presented show the feasibility of prolonged use of HFNO and APP in patients with COVID-19 and severe persistent hypoxemia and described strategies to enhance adherence.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".