Abstract 9997: Utilizing a Collaborative Learning Model to Decrease Length of Postoperative Ventilation Following Infant Heart Surgery
Bibliographic record
Abstract
Introduction: Collaborative learning utilizes shared experience and expertise to rapidly disseminate knowledge across systems. We employed this strategy to reduce the duration of mechanical ventilation following infant heart surgery. Hypothesis: A collaborative learning derived clinical practice guideline (CPG) will result in a significant increase in the rate of early extubation (EE) following surgical repair of tetralogy of Fallot (TOF) or isolated coarctation (Coarct) in infants. Methods: A collaborative learning initiative that included site visits was employed at five centers (active sites) in the Pediatric Heart Network (PHN). Participants developed a CPG to achieve EE (within 6 hours of surgery) for infants following 2 index operations: 1) repair of isolated Coarct and 2) repair of TOF (30-365 days). EE rates were calculated at the five active sites during the 12 months before and after CPG implementation, and at five other centers in the PHN not participating in collaborative learning or using the CPG (control sites). A differences analysis was performed to compare changes in EE after CPG implementation. Results: Among the five active sites, there were 322 subjects that met eligibility for the CPG (163 pre- and 159 post-implementation). Among the five control centers, there were 259 subjects that would meet eligibility for the CPG (120 pre- and 139 post-implementation). With CPG implementation the rate of EE increased by 44.8% among active sites, from 28.8% to 73.6% (p<0.001). No change in the rate of EE was found in the control sites 11.7% to 13.7% (p=0.58). At active sites, there was no change in the rate of reintubation within 48 hours (3.1% v 3.1%, p=0.97). At active sites there was no statistically significant impact on median intensive care unit (3.0 days pre- vs. 2.9 post-implementation, p=0.6) or total postoperative length of stay (PLOS)(6 days pre- vs. 5 post-implementation, p=0.5) with CPG implementation. Conclusions: A collaborative learning strategy designed to shorten postoperative mechanical ventilation led to a significant increase in the rates of EE with no change in the rate of reintubation. The EE CPG did not significantly change intensive care or total PLOS.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".