The utilization of health care resources by children born with a congenital surgical anomaly – the utility of a physician assistant
Bibliographic record
Abstract
Introduction: Children born with a congenital surgical anomaly require surgical correction and the care of a multidisciplinary team. Medical and surgical innovation has led to improved survival rates, exposing these children to morbidities, ultimately leading to increased healthcare costs. Physician assistants (PAs) are medical generalists that extend the services of physicians, improve access to care and decrease healthcare costs. Methods: We performed a retrospective cohort study of children born with a congenital surgical anomaly between 1990-2017 using the Winnipeg Surgical Database of Outcomes and Management (WiSDOM) and the Necrotizing Enterocolitis Management and Outcomes (NEMO) database; a 10:1 date-of-birth matched control population was selected using the Manitoba Centre for Health Policy (MCHP). The median cost of consult services, procedures and follow-up services was compared for cases versus controls. A comparative advantage analysis was performed to examine the cost-effectiveness of PAs. Results: Cases generally had a higher median cost for consults, procedures and follow-up services compared to controls. For services provided specifically by general surgeons, cases were found to incur a higher cost primarily for major surgeries. PAs were found to be twice as cost effective as doctors at providing approximately 75% of the care of these patients. Conclusion: Children born with a congenital surgical anomaly had higher healthcare costs than controls. Most of this excess cost is incurred in consult services, major surgeries and follow-up services provided by specialists other than general surgeons. The implementation of PAs into the care of these patients is an effective means of reducing these costs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".