Does marginalization status and distance from the hospital impact the timing of pediatric pyeloplasty in a universal access-to-care system
Bibliographic record
Abstract
INTRODUCTION: Prior research in the U.S. suggests that marginalized populations tend to undergo pyeloplasty earlier, likely reflecting concerns about the challenges of reliable followup care in this population. This study assessed the effect of sociodemographic marginalization and geographic distance on the timing of pyeloplasty in a universal, single-payer healthcare system. METHODS: We performed a single-center, retrospective study on children undergoing pyeloplasty from 2008-2019. We assessed the impact of marginalization indices (with the Ontario Marginalization Index [ON-Marg]) and geographic distance on timing of pyeloplasty, preoperative ultrasound findings, and renogram features. Unadjusted analysis and Cox proportional regression were performed to determine the effect of marginalization and geography on timing of pyeloplasty. RESULTS: Among 503 patients who underwent pyeloplasty, the median age at surgery was 16 months. Median preoperative anteroposterior diameter (APD) was 22 mm, and split renal function on renogram was 43%. There was no significant effect of marginalization indices on age at surgery. Patients living >50 km from the hospital had a later age at surgery than those living <10 km (p=0.04); however, there was no significant difference in preoperative APD or split function by marginalization index or geographic distance. Adjusted analyses revealed no significant associations between ON-Marg variables or geographic distance with age at surgery. CONCLUSIONS: In a universal healthcare system, marginalization status and geographic distance were not associated with the timing of pyeloplasty. These results contrast with literature from other settings and suggest that access to care can mitigate disparities in pediatric urology care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".