Developing a Value-Based Approach to Outcome Reporting in Pediatric Surgery
Bibliographic record
Abstract
The population that undergoes pediatric surgical procedures in high-resource settings such as Canada primarily comprises healthy patients who undergo low-risk, elective surgeries and fewer higher-risk patients who require more complex surgeries. Given this variability, there is a relatively low incidence of traditionally measured "critical" outcomes within any single pediatric surgical system or even pediatric surgical subspecialty, rendering the currently available quality measurement tools inadequate to provide sensitive measures of quality. In an era when scalable solutions are required to improve health outcomes across entire populations, there is an urgent need for more holistic measures of a child's well-being to benchmark and measure changes in quality of care. This article discusses opportunities for enhanced performance measurement in pediatric surgery using a value-based framework to identify and measure patient and family outcomes of importance over the full care cycle, from initial presentation through surgery and recovery to sustainability of health. In suggesting new avenues for performance measurement, we highlight how these measures can be used to develop, evaluate and refine surgical system innovations such as bundled care pathways and perioperative care homes.
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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 | 0.243 | 0.424 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.005 | 0.010 |
| 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".