The Impact of Hospital Surgical Volume on Healthcare Utilization Outcomes After Pediatric Thyroidectomy
Bibliographic record
Abstract
BACKGROUND: A positive relationship between an individual surgeon's operative volume and clinical outcomes after pediatric and adult thyroidectomy is well-established. The impact of a hospital's pediatric operative volume on surgical outcomes and healthcare utilization, however, are infrequently reported. We investigated associations between hospital volume and healthcare utilization outcomes following pediatric thyroidectomy in Canada's largest province, Ontario. METHODS: Retrospective analysis of administrative and health-related population-level data from 1993 to 2017. A cohort of 1908 pediatric (<18 years) index thyroidectomies was established. Hospital volume was defined per-case as thyroidectomies performed in the preceding year. Healthcare utilization outcomes: length of stay (LOS), same day surgery (SDS), readmission, and emergency department (ED) visits were measured. Multivariate analysis adjusted for patient-level, disease and hospital-level co-variates. RESULTS: Hospitals with the lowest volume of pediatric thyroidectomies, accounted for 30% of thyroidectomies province-wide and performed 0-1 thyroidectomies/year. The highest-volume hospitals performed 19-60 cases/year. LOS was 0.64 days longer in the highest, versus the lowest quartile. SDS was 83% less likely at the highest, versus the lowest quartile. Hospital volume was not associated with rate of readmission or ED visits. Increased ED visits were, however, associated with male sex, increased material deprivation, and rurality. CONCLUSIONS: Increased hospital pediatric surgical volume was associated with increased LOS and lower likelihood of SDS. This may reflect patient complexity at such centers. In this cohort, low-volume hospitals were not associated with poorer healthcare utilization outcomes. Further study of groups disproportionately accessing the ED post-operatively may help direct resources to these populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".