Healthcare utilization by patients with primary hyperparathyroidism
Bibliographic record
Abstract
INTRODUCTION: Urolithiasis is a common complication of primary hyperparathyroidism (PHPT). Parathyroidectomy has been shown to decrease the rate of stone formation. The purpose of this study was to evaluate healthcare resource utilization before and after parathyroidectomy and identify predictors of increased healthcare utilization. METHODS: A retrospective analysis of patients who had a parathyroidectomy for PHPT in Nova Scotia from 2013-2018 was performed. Data from five years before parathyroidectomy to three years after were included. Outcomes included emergency department (ED) visits and the number of urologic interventions. Random-effects Poisson regression models were used to calculate the primary outcomes, ED visits, and the number of urologic interventions while adjusting for prespecified characteristics. RESULTS: Fifty patients (62% female) with a mean age of 60±11 years were identified. ED visits were 0.42 per year before parathyroidectomy and 0.20 per year after in a multivariate analysis (incidence rate ratio [IRR] 0.48, confidence interval [CI] 0.25-0.91, p=0.024). There was no statistical difference between male and female ED visits (p=0.6719). There was no difference in the rate of ED visits for non-urologic reasons after parathyroidectomy (p=0.0749). The incidence of urologic intervention for stones was 1.24 per year before parathyroidectomy and 0.53 per year after (IRR 0.42, CI 0.26-0.68, p=0.0005). CONCLUSIONS: Healthcare resource utilization, in terms of ED visits and urologic intervention, significantly decreased after parathyroidectomy. Sex showed no statistical difference in predicting healthcare utilization, while non-urologic ED visits remained the same after surgery. Expedited parathyroidectomy for PHPT patients may decrease urologic interventions and ED visits, resulting in less healthcare utilization.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".