An international factorial vignette-based survey of intubation decisions in acute hypoxemic respiratory failure
Bibliographic record
Abstract
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.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".