Feasibility of implementing <i>Extubation Advisor</i>, a clinical decision support tool to improve extubation decision-making in the ICU: a mixed-methods observational study
Bibliographic record
Abstract
OBJECTIVES: Although spontaneous breathing trials (SBTs) are standard of care to extubation readiness, no tool exists that optimises prediction and standardises assessment. In this study, we evaluated the feasibility and clinical impressions of Extubation Advisor (EA), a comprehensive clinical extubation decision support (CDS) tool. DESIGN: Phase I mixed-methods observational study. SETTING: Two Canadian intensive care units (ICUs). PARTICIPANTS: We included patients on mechanical ventilation for ≥24 hours and clinicians (respiratory therapists and intensivists) responsible for extubation decisions. INTERVENTIONS: Components included a predictive model assessment, feasibility evaluation, questionnaires and interviews with clinicians. RESULTS: We enrolled 117 patients, totalling 151 SBTs and 80 extubations. The incidence of extubation failure was 11% in low-risk patients and 21% in high-risk patients stratified by the predictive model; 38% failed extubation when both the model and clinical impression were at high risk. The tool was well rated: 94% and 75% rated the data entry and EA report as average or better, respectively. Interviews (n=15) revealed favourable impressions regarding its user interface and functionality, but unexpectedly, also concerns regarding EA's potential impact on respiratory therapists' job security. CONCLUSIONS: EA implementation was feasible, and users perceived it to have potential to support extubation decision-making. This study helps to understand bedside implementation of CDS tools in a multidisciplinary ICU. TRIAL REGISTRATION NUMBER: NCT02988167.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".