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Record W4394904439 · doi:10.1101/2024.04.16.24305906

An international factorial vignette-based survey of intubation decisions in acute hypoxemic respiratory failure

2024· preprint· en· W4394904439 on OpenAlexafffundabout
Christopher J. Yarnell, Arviy Paranthaman, Peter M. Reardon, Federico Angriman, Thiago Bassi, Giacomo Bellani, Laurent Brochard, Harm‐Jan de Grooth, Laura Dragoi, Syafruddin Gaus, Paul Glover, Ewan C. Goligher, Kimberley Lewis, Baoli Li, Hashim Kareemi, Bharath Kumar Tirupakuzhi Vijayaraghavan, Sangeeta Mehta, Ricard Mellado Artigas, Julie Moore, Idunn S. Morris, Georgiana Roman-Sarita, Tài Pham, Jariya Sereeyotin, George Tomlinson, Hannah Wozniak, Takeshi Yoshida, Rob Fowler

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsMcMaster UniversitySt. Michael's HospitalUniversity Health NetworkUniversity of TorontoSinai Health SystemSunnybrook Health Science CentreUniversity of British ColumbiaThe Scarborough HospitalHealth Sciences Centre
FundersEuropean Society of Intensive Care MedicineIntensive Care SocietyCanadian Association of Emergency Physicians
KeywordsMedicineIntubationVignetteOddsOdds ratioEmergency medicineAnesthesiaLogistic regressionInternal medicineStatistics

Abstract

fetched live from OpenAlex

Abstract Purpose Intubation is a common procedure in acute hypoxemic respiratory failure (AHRF), with minimal evidence to guide decision-making. We conducted a survey of when to intubate patients with AHRF to measure the influence of clinical variables on intubation decision-making and quantify variability. Methods We developed an anonymous factorial vignette-based web survey to ask clinicians involved in the decision to intubate “Would you recommend intubation?” Respondents selected an ordinal recommendation from a 5-point scale ranging from “Definite no” to “Definite yes” for up to 10 randomly allocated vignettes. We disseminated the survey through clinical and academic societies, analyzed responses using Bayesian proportional odds modeling with clustering by individual, country, and region, and reported mean odds ratios (OR) with 95% credible intervals (CrI). Results Between September 2023 and January 2024, 2,294 respondents entered 17,235 vignette responses in 74 countries [most common: Canada (29%), USA (26%), France (9%), Japan (8%), and Thailand (5%)]. Respondents were attending physicians (63%), nurses (13%), trainee physicians (9%), respiratory therapists (9%), other (6%). Lower oxygen saturation, higher inspired oxygen fraction, non-invasive ventilation compared to high-flow, tachypnea, neck muscle use, abdominal paradox, drowsiness, and inability to obey were associated with increased odds of intubation; diagnosis, vasopressors, and duration of symptoms were not. Within a country the odds of recommending intubation changed between clinicians by an average factor of 2.60, while changing between countries within a region changed it by 1.56. Conclusion In this international, interprofessional survey of more than 2000 practicing clinicians, intubation for patients with AHRF was mostly decided based on oxygenation, breathing pattern, and consciousness, but there was important variation across individuals and countries.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.056
GPT teacher head0.348
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2024
Admission routes3
Has abstractyes

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