<i>Extubation Advisor</i> : Implementation and Evaluation of A Novel Extubation Clinical Decision Support Tool
Bibliographic record
Abstract
Importance: Extubation Advisor (EA) is a novel software tool that generates a synoptic report for each Spontaneous Breathing Trial (SBT) conducted to inform extubation decision-making. Objectives: To assess bedside EA implementation, perceptions of utility, and identify barriers and facilitators of use. Design, Setting and Participants: We conducted a phase I mixed-methods interventional study in three mixed intensive care unit (ICUs) in two academic hospitals. We interviewed critical care physicians (MDs) and respiratory therapists (RTs) regarding user-centered design principles and usability. Analysis: We evaluated our ability to consent participants (feasibility threshold 50%), capture complete data (threshold 90%), generate and review EA reports in real-time (thresholds 75% and 80%, respectively), and MD perception of tool usefulness (6-point Likert scale). We analyzed interview transcripts using inductive coding to identify facilitators and barriers to EA implementation and perceived benefit of tool use. Results: We enrolled 31 patients who underwent 70 SBTs. Although consent rates [31/31 (100%], complete data capture [68/68 (100%)], and EA report generation [68/70 (97.1%)] exceeded feasibility thresholds, reports were reviewed by MDs for [55/70 (78.6%)] SBTs. Mean MD usefulness score was 4.0/6. Based on feedback obtained from 36 interviews (15 MDs, 21 RTs), we revised the EA report twice and identified facilitators (ability to track patient progress, enhance extubation decision-making, and provide support in resource-limited settings) and barriers (resource constraints, need for education) to tool implementation. Half of respondents (9 MDs, 9 RTs; combined 50%) perceived definite or potential benefit to EA tool use. Conclusion: This is the first study of a waveform-based variability-derived, predictive clinical decision support tool evaluated in adult ICUs. Our findings support the feasibility of integrating the EA tool into bedside workflow. Clinical trials are needed to assess the utility of the EA tool in practice and its impact on extubation decision-making and outcomes.Trial RegistrationNCT04708509.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".