International Survey of High-Flow Nasal Therapy Use for Respiratory Failure in Adult Patients
Bibliographic record
Abstract
(1) Background: High-flow nasal therapy (HFNT) has shown several benefits in addressing respiratory failure. However, the quality of evidence and the guidance for safe practice are lacking. This survey aimed to understand HFNT practice and the needs of the clinical community to support safe practice. (2) Method: A survey questionnaire was developed and distributed to relevant healthcare professionals through national networks in the UK, USA and Canada; responses were collected between October 2020 and April 2021. (3) Results: In the UK and Canada, HFNT was used in 95% of hospitals, with the highest use being in the emergency department. HNFT was widely used outside of a critical care setting. HFNT was mostly used to treat acute type 1 respiratory failure (98%), followed by acute type 2 respiratory failure and chronic respiratory failure. Guideline development was felt to be important (96%) and urgent (81%). Auditing of practice was lacking in 71% of hospitals. In the USA, HFNT was broadly similar to UK and Canadian practice. (4) Conclusions: The survey results reveal several key points: (a) HFNT is used in clinical conditions with limited evidence; (b) there is a lack of auditing; (c) it is used in wards that may not have the appropriate skill mix; and (d) there is a lack of guidance for HFNT use.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".