Initial Assessment and Monitoring of Patients with Chronic Hypoparathyroidism: A Systematic Current Practice Survey
Bibliographic record
Abstract
Chronic hypoparathyroidism (HypoPT) is associated with significant morbidity and impaired quality of life (QoL). The goals of management for chronic HypoPT include improvement in QoL and the prevention of both hypo- and hypercalcemia symptoms and long-term complications. Several groups have provided consensus statements and guidelines on the management of HypoPT; however, due to limited evidence, these recommendations have largely been based on literature reviews, expert opinion, and consensus statements. The objective of this study was to use a systematic approach to describe current practice for the initial assessment and follow-up of patients with chronic HypoPT. We developed a survey asking experts in the field to select the responses that best reflect their current practice. The survey found no differences in responses between nonsurgical and postsurgical patient assessment. For new patients, respondents usually performed an assessment of serum lab profile (calcium [either albumin-adjusted or ionized], magnesium, creatinine, phosphate, 25-hydroxyvitamin D), 24-hour urine (creatinine, calcium), and a renal ultrasound to evaluate for the presence of nephrocalcinosis or nephrolithiasis. For follow-up patients, most respondents perform blood tests and urine tests every 6 months or less frequently. The reported clinical practice patterns for monitoring for complications of chronic HypoPT vary considerably among respondents. Based on the responses in this systematic expert practice survey, we provide practice suggestions for initial assessment and follow-up of patients with chronic HypoPT. In addition, we highlight areas with significant variation in practice and identify important areas for future research. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".