A Regional Surgical Partnership Program: Lessons Learned in System Transformation of Pediatric Surgical Care
Bibliographic record
Abstract
The province of Ontario has seen significant growth in the waitlists for both pediatric surgery and pediatric endoscopy. Due to long-standing resource constraints exacerbated by the COVID-19 pandemic, the surgical waitlist at a pediatric tertiary hospital in Toronto had risen to over 6,500 patients by April 2023, with 65% beyond nationally validated wait time targets (out-of-window). A regional Surgical and Endoscopy Community Partnerships program was developed with five partner hospital sites to build capacity and decentralize pediatric surgical care by transferring select low-acuity patients, primarily targeting the longest-waiting cases, from the pediatric tertiary hospital waitlists to partner hospitals closer to their homes. Each of the partner hospitals had pre-existing pediatric surgical programs and the necessary infrastructure and staffing to support the referred patients. Between April 2023 and March 2024, this program transitioned more than 650 pediatric cases to partner hospitals, reducing the waitlist by approximately 10%. Early program success has demonstrated that an integrated system-wide approach to the provision of pediatric surgical care is a viable model for future surgical care delivery.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".