High-flow nasal oxygen in acute hypoxemic respiratory failure: A narrative review of the evidence before and after the COVID-19 pandemic
Bibliographic record
Abstract
High-flow nasal oxygen (HFNO) is a type of non-invasive advanced respiratory support that allows the delivery of high-flow and humidified air through a nasal cannula. It can deliver a higher inspired oxygen fraction than conventional oxygen therapy (COT), improves secretion clearance, has a small positive end-expiratory pressure, and exhibits a washout effect on the upper air space that diminishes dead space ventilation. HFNO has been shown to reduce the work of breathing in acute hypoxemic respiratory failure (AHRF) and has become an interesting option for non-invasive respiratory support. Evidence published before the COVID-19 pandemic suggested a possible reduction of the need for invasive mechanical ventilation compared to COT. The COVID-19 pandemic has resulted in a substantial increase in AHRF worldwide, overwhelming both acute and intensive care unit capacity in most countries. This triggered new trials, adding to the body of evidence on HFNO in AHRF and its possible benefits compared to COT or non-invasive ventilation. We have summarized and discussed this recent evidence to inform the best supportive strategy in AHRF both related and unrelated to COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 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".