Biliary Atresia in Canada: The Effect of Centre Caseload Experience on Outcome
Bibliographic record
Abstract
OBJECTIVES: : Biliary atresia (BA) is a leading cause of end-stage paediatric liver disease. Standard BA treatment is sequential surgery with an initial Kasai procedure (KP) followed by liver transplant (LT) for patients who progress to liver failure. A key determinant for the post-KP patient survival with their native liver is patient age at KP (older age, poorer outcome). Recently, European studies have reported that caseload experience influences prognosis with centres managing <5 cases per year (UK) or <2 cases per year (France) having worse survival. Our study investigates the effect of caseload experience on outcomes of Canadian patients with BA. PATIENTS AND METHODS: : A national database of cases with BA, born from 1992 to 2002, was examined. Patients were grouped according to treatment centre size (A: on average <1 case per year; B: 1 to 3 cases per year; and C: >3 cases per year). Overall patient, post-KP native liver, and LT survivals were compared between centres. Outcome parameters were reevaluated for patients grouped by the largest Canadian centre (>5 cases per year) and all other centres (<5 cases per year). RESULTS: : Two-hundred thirty patients were identified among 6 group A, 4 group B, and 2 group C centres. The overall median age at KP was 64 days. There were no significant differences in patient, post-KP native liver, or LT survivals between the sized centres and even the largest centre, with the overall 4-year post-KP native liver survival being 39%. CONCLUSIONS: : Caseload experience does not importantly affect the outcomes for Canadian children with BA. Although outcomes in Canada are comparable to those elsewhere, national policies directed towards timely referral and earlier age at KP rather than centralisation of care are needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".