Abstract P249: Prospective Monitoring of Pediatric Cardiac Surgery Program Complications
Bibliographic record
Abstract
Background: The next horizon for improving pediatric cardiac surgery outcomes is the standardization and systematic tracking of complications. Methods: IWK REB approval was obtained. The Multisocietal Database Committee short list of complications (52) were captured prospectively for all pediatric cardiac operations at the IWK Oct 1 2009-Sept 30 2010. Morbidity burden was calculated by multiplying a severity coefficient (1-3) by frequency of complication in each surgical complexity strata using RACHS categories. Death was included as a complication (severity coefficient= 5). Indexed morbidity was calculated for each RACHS category by dividing morbidity burden by number of procedures. Results: A total of 110 procedures were performed on 86 patients. 83 of the 86 index procedures were open and 22 of the patients were neonates. The procedural mortality rate was 3.6%. Forty one (41/86, 47.7%) of the index procedures had a total of 89 complications. Sixty-four patients were in RACHS category 2 or 3. The most common complications were pulmonary (22) arrhythmias (16), or operative (16), accounting for 61%(54/89) of total complications. The indexed morbidity was 0 for RACHS1, 1.55 for RACHS 2, 2.63 for RACHS 3, 1.81for RACHS 4, and 2.81 for RACHS5/6. The CUSUM plots the occurrence of any complication in each case versus morbidity burden, illustrating the effect of complication severity on the slope of the curve. Conclusions: A high rate of perioperative complications are recorded when tracked prospectively using standardized definitions. Adjusting complications for severity may identify areas for improving patient outcomes that are missed by simply recording their occurrence.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".