Bibliographic record
Abstract
There has been a recent trend of improved outcomes for most infants born with surgically correctable congenital malformations, despite the fact that current surgical treatment is not standardized, with wide variations in practice among institutions. Because care for these infants is multidisciplinary, it is difficult to ascertain with clarity the specific role of neonatal surgery in determining outcomes. Moreover, the lack of validated measures of illness severity for most complex congenital malformations makes risk adjustment difficult. For these reasons, the utility of randomized controlled trials in determining best surgical practice in neonatal surgery for congenital malformations is impractical, and another means of deriving medical evidence to justify 'optimal' treatment is necessary.The Canadian Paediatric Surgical Network (CAPSNet) was developed specifically to address these issues. Patterned after the highly successful Canadian Neonatal Network, CAPSNet collects standardized data on every case of gastroschisis and congenital diaphragmatic hernia evaluated in the 16 referral perinatal centres in Canada. These centres serve as provincial referral centres for perinatal care, and, therefore, the data set created is population-based for gastroschisis and congenital diaphragmatic hernia in Canada. In addition to neonatal data fields recorded in the Canadian Neonatal Network, CAPSNet collects specific prenatal data, and details on surgical treatment and outcomes within each of the 16 participating centres. It is hoped that by using advanced analytical techniques, including outcomes modelling and multiple logistic regression analysis of risk-adjusted outcome variations by type of surgery performed, optimal treatment paradigms will be identified that will lead to further outcome improvement in babies born with complex birth defects.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".