Consensus definitions of complications for accurate recording and comparisons of surgical outcomes in pediatric neurosurgery
Bibliographic record
Abstract
OBJECT: Monitoring and recording of complications in pediatric neurosurgery are important for quality assurance and in particular for improving outcomes. Lack of accurate or mutually agreed upon definitions hampers this process and makes comparisons between centers, which is an important method to improve outcomes, difficult. Therefore, the Canadian Pediatric Neurosurgery Study Group created definitions of complications in pediatric neurosurgery with consensus among 13 Canadian pediatric neurosurgical centers. METHODS: Definitions of complications were extracted from randomized trials, prospective data collection studies, and the medical literature. The definitions were presented at an annual meeting and were subsequently recirculated for anonymous comment and revision, assembled by a third party, and re-presented to the group for consensus. RESULTS: Widely used definitions of shunt failure were extracted from previous randomized trials and prospective studies. Definitions for wound infections were extracted from the definitions from the Centers for Disease Control and Prevention. Postoperative neurological deficits were based on the Pediatric Stroke Outcome Measure. Other definitions were created and modified by consensus. These definitions are now currently in use across the Canadian Pediatric Neurosurgery Study Group centers in Morbidity and Mortality data collection and for subsequent comparison studies. CONCLUSIONS: Coming up with consensus definitions of complications in pediatric neurosurgery is a first step in improving the quality of outcomes. It is a dynamic process, and further refinements are anticipated. Center to center comparison will hopefully allow significant variations in outcomes to be identified and acted upon.
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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 | 0.237 | 0.414 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".