Pattern of Laboratory Parameters and Management of Secondary Hyperparathyroidism in Countries of Europe, Asia, the Middle East, and North America
Bibliographic record
Abstract
INTRODUCTION: This analysis explored laboratory mineral and bone disorder parameters and management of secondary hyperparathyroidism in patients undergoing hemodialysis in Belgium, Canada, China, France, Germany, Italy, Japan, Russia, Saudi Arabia, Spain, Sweden, the UK, and the USA. METHODS: Analyses used demographic, medication, and laboratory data collected in the prospective Dialysis Outcomes and Practice Patterns Study (2012-2015). The analysis included 20,612 patients in 543 facilities. Descriptive data are presented as regional mean (standard deviation), median (interquartile range), or prevalence, weighted for facility sampling fraction. No testing of statistical hypotheses was conducted. RESULTS: The frequency of serum intact parathyroid hormone levels > 600 pg/mL was lowest in Japan (1%) and highest in Russia (30%) and Saudi Arabia (27%). The frequency of serum phosphorus levels > 7.0 mg/dL was lowest in France (4%), the UK (6%), and Spain (6%), and highest in China (27%). The frequency of serum calcium levels > 10.0 mg/dL was highest in the UK (14%) and China (13%) versus 2% to 9% elsewhere. Dialysate calcium concentrations of 2.5 mEq/mL were common in the USA (78%) and Canada (71%); concentrations of 3.0-3.5 mEq/L were almost universal at facilities in Italy, France, and Saudi Arabia (each ≥ 99%). CONCLUSIONS: Wide international variation in mineral and bone disorder laboratory parameters and management practices related to secondary hyperparathyroidism suggests opportunities for optimizing 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 | 0.001 |
| 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) | 0.001 | 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".