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Record W4410043051 · doi:10.1038/s41598-025-99369-y

Transformers for rapid detection of airway stenosis and stridor

2025· article· en· W4410043051 on OpenAlexaff
James Anibal, Rebecca Doctor, Micah Boyer, Iris De Santiago, Shaheen N. Awan, Yassmeen Abdel‐Aty, Gregory R. Dion, Veronica Daoud, Hannah Huth, Stephanie Watts, Bradford J. Wood, David A. Clifton, Alexander Gelbard, Maria Powell, Jamie Toghranegar

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsMount Sinai HospitalHospital for Sick ChildrenDalhousie UniversityUniversity of TorontoSimon Fraser UniversityUniversité de Montréal
FundersNIH Clinical CenterNational Institute of Allergy and Infectious DiseasesColgate-Palmolive CompanyNational Heart, Lung, and Blood InstituteGenentechUniversity of OxfordNational Institutes of HealthRoyal Academy of EngineeringWellcome TrustEngineering and Physical Sciences Research CouncilNational Institute for Health and Care ResearchCommon FundAmerican Association for Dental, Oral, and Craniofacial ResearchNational Cancer Institute
KeywordsStridorMedicineAirwayStenosisRespiratory soundsIntubationAsthmaCardiologyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Upper airway stenosis is a potentially life-threatening condition involving the narrowing of the airway. In more severe cases, airway stenosis may be accompanied by stridor, a type of disordered breathing caused by turbulent airflow. Patients with airway stenosis have a higher risk of airway failure and additional precautions must be taken before medical interventions like intubation. However, stenosis and stridor are often misdiagnosed as other respiratory conditions like asthma/wheezing, worsening outcomes. This report presents a unified dataset containing recorded breathing tasks from patients with stridor and airway stenosis. Customized transformer-based models were also trained to perform stenosis and stridor detection tasks using low-cost data from multiple acoustic prompts recorded on common devices. These methods achieved AUC scores of 0.875 for stenosis detection and 0.864 for stridor detection, demonstrating the potential to add value as screening tools in real-world clinical workflows, particularly in high-volume settings like emergency departments.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2025
Admission routes1
Has abstractyes

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