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Record W4409167433 · doi:10.1007/s44337-025-00290-0

Research hotspots and publication trends of high flow nasal oxygen: a bibliometric analysis from 2004 to 2023

2025· article· en· W4409167433 on OpenAlexaboutno aff
Yongbo Duan, Zhaoxu Ran, Xiaoying Xu

Bibliographic record

VenueDiscover Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsnot available
FundersLanzhou Science and Technology Bureau
KeywordsEnvironmental scienceBibliometricsComputer scienceLibrary science

Abstract

fetched live from OpenAlex

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.

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

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 armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0820.227
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.033
GPT teacher head0.363
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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