Effects of Social Disparities on Management and Surgical Outcomes for Patients with Secondary Hyperparathyroidism
Bibliographic record
Abstract
INTRODUCTION: Nearly 80% of chronic renal failure patients have secondary hyperparathyroidism. Cinacalcet is used to lower parathyroid hormone; however, it is expensive and has side effects. When secondary hyperparathyroidism is resistant to medication or medications are inaccessible, parathyroidectomy is performed. Race and socioeconomic status influence access to care and surgical outcomes. We sought to evaluate the effect of race and socioeconomic status on parathyroidectomy rate as well as surgical outcomes of patients with secondary hyperparathyroidism. METHODS: We undertook cross-sectional analysis of adults diagnosed with secondary hyperparathyroidism in the USA between 2012 and 2014, using the National Inpatient Sample. Univariate and multivariate analyses were used to determine associations between social disparities, likelihood to undergo parathyroidectomy, and surgical outcomes. RESULTS: Between 2012 and 2014, a national estimate of 724,170 hospitalizations were identified where patients had a diagnosis of secondary hyperparathyroidism. Operative rate was 0.67%. By socioeconomic status, differences in rates of surgery in the poorest compared to the richest were not significant (0.74% vs. 0.55%, OR 1.08, p = 0.5). African-American patients had higher rates of parathyroidectomy compared to Caucasians (1 vs. 0.74%, OR 1.49, p < 0.001). African-American patients also had a trend toward more complications and greater length of stay. CONCLUSIONS: According to a large administrative dataset, parathyroidectomy for secondary hyperparathyroidism is seldom used in the USA. African-American patients have higher rates of surgical management. Surgical outcomes may be affected by race. Clinicians treating secondary hyperparathyroidism should be aware of existing disparities within their health system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".