Research hotspots and publication trends of high flow nasal oxygen: a bibliometric analysis from 2004 to 2023
Bibliographic record
Abstract
High-flow nasal oxygen (HFNO) has become an essential respiratory support modality in anesthesia, intensive care, and emergency medicine. This bibliometric analysis systematically evaluates the research landscape and technological developments in HFNO monitoring and management. Relevant publications were retrieved from the Web of Science Core Collection database using a comprehensive search strategy. Bibliometric analysis and visualization of countries, institutions, authors, journals, and keywords were performed using CiteSpace, VOSviewer, and Scimago Graphica. The analysis covered publications from 2004 to 2023. A total of 2782 publications on HFNO were identified. The United States was the leading country in terms of research output, while the University of Toronto was the most productive institution. Jie Li was the most prolific author, and Respiratory Care was the journal with the highest number of publications. The most common keywords included “COVID-19”, “noninvasive ventilation”, “high-flow nasal cannula”, “therapy”, and “ventilation”. Keyword emergence testing revealed that Transnasal Humidified Rapid-Insufflation Ventilatory Exchange (THRIVE) is a recent research hotspot. The study reveals a significant increase in HFNO research publications worldwide, a trend that is expected to continue. Future research should focus on exploring HFNO applications in diverse clinical settings, optimizing personalized treatment strategies, and integrating other respiratory support techniques to enhance its efficacy and safety.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.082 | 0.227 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".