Practising evidence-based medicine: the design and implementation of a multidisciplinary team-driven extubation protocol
Bibliographic record
Abstract
BACKGROUND: Evidence from recent literature shows that protocol-directed extubation is a useful approach to liberate patients from mechanical ventilation (MV). However, research evidence does not necessarily provide guidance on how to implement changes in individual intensive care units (ICUs). We conducted the present study to determine whether such an evidence-based strategy can be implemented safely and effectively using a multidisciplinary team (MDT) approach. METHOD: We designed a MDT-driven extubation protocol. Multiple meetings were held to encourage constructive criticism of the design by attending physicians, nurses and respiratory care practitioners (RCPs), in order to define a protocol that was evidence based and acceptable to all clinical staff involved in the process of extubation. It was subsequently implemented and evaluated in our medical/ surgical ICU. Outcomes included response of the MDT to the initiative, duration of MV and stay in the ICU, as well as reintubation rate. RESULTS: The MDT responded favourably to the design and implementation of this MDT-driven extubation protocol, because it provided greater autonomy to the staff. Outcomes reported in the literature and in the historical control group were compared with those in the protocol group, and indicated similar durations of MV and ICU stay, as well as reintubation rates. No adverse events were documented. CONCLUSION: An MDT approach to protocol-directed extubation can be implemented safely and effectively in a multidisciplinary ICU. Such an effort is viewed favourably by the entire team and is useful in enhancing team building.
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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 | 0.422 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| 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".