High Prevalence of Hyperparathyroidism among Peritoneal Dialysis Patients: A Review of 176 Patients
Bibliographic record
Abstract
OBJECTIVES: Parathyroid dysfunction continues to produce significant morbidity in dialysis patients. Since the introduction of low calcium dialysate for peritoneal dialysis (PD), no large studies have been done to determine the prevalence of parathyroid dysfunction in these patients. This study was done to assess the prevalence of parathyroid disease in the PD population and to determine the risk factors associated with this dysfunction. DESIGN: We analyzed data on 176 patients who received PD at a single center between August 1998 and February 1999. Clinical data, laboratory variables related to parathyroid function, and data pertaining to dialysis treatment and weekly drug dosing were obtained for each patient on two different occasions, approximately 3 months apart. Variables predictive of the development of parathyroid dysfunction were calculated by univariate and multivariate logistic regression analysis. RESULTS: Two-thirds of the patients surveyed had an abnormal intact parathyroid hormone (iPTH) level: 47% had an iPTH level more than three times normal, the mean was 54.6+/-35.4 pmol/L; 23% had an iPTH value below the upper limit of normal, here the mean was 3.6+/-1.8 pmol/L. Diabetic patients had lower iPTH levels (22.2+/-28.4 pmol/L) than nondiabetics (33.9+/-34.8 pmol/L) (p = 0.02). On multivariate regression analysis, we found that age, duration of dialysis, Kt/V, serum bicarbonate, and serum ionized calcium levels did not significantly affect parathyroid function. Hyperphosphatemia was the only factor that was associated with the development of secondary hyperparathyroidism in this study population (p = 0.029). CONCLUSION: There is a high prevalence of hyperparathyroidism in the current PD population. Phosphate control is suboptimal and hyperphosphatemia is an independent risk factor for the development of hyperparathyroidism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".