Factors Influencing the Parathyroid Hormone Response in Hemodialysis Patients
Bibliographic record
Abstract
PURPOSE OF STUDY: Adynamic bone disease (ABD) indicates a significant risk factor for morbidity and mortality, therefore risk factors for ABD should be investigated. In this study we aimed to determine the contributing factors for ABD and parathyroid hormone (iPTH) response in HD patients. METHODS: A series of 139 patients (age: 47.8 ± 13.8 years, HD duration: 67.1 ± 52.7 months) were included. Five years data about the patients clinical therapy and cumulative calcium dose) and laboratory features were collected. Patients were divided into three groups as those with low, intermediate and high iPTH: Group I (iPTH<150 pg/ml, n:43), Group II (iPTH: 150–300 pg/ml, n:37) and Group III (iPTH: > 300 pg/ml n:59). We excluded the patients who received calcitriol therapy after the initiation of HD from group I. RESULTS: When the groups were compared, patients in-group I had significantly shorter HD duration (54.5 ± 40.1 and 72.8 ± 54.3 months, p < 0.04), higher mean calcium and lower phosphorus levels (p < 0.0001, p < 0.0001), higher total cholesterol (p < 0.001), CRP (p < 0.02), fibrinogen (p < 0.005), and ferritin (p < 0.01) levels than those in-group II and III. A higher calcium phosphorus product (44.7 ± 12.5 and 50.9 ± 12.4, p < 0.0005) and presence of rHuepo resistance (p < 0.003) were strikingly different between Group II and III. According to the multivariate analysis, follow-up data of higher calcium (p < 0.0001) and fibrinogen (p < 0.03); lower phosphorus (p < 0.001) levels were significant determinants of low PTH levels. CONCLUSION: Even small changes in calcium phosphorus balance can influence a wide spectrum of parathyroid hormone response in long-term follow-up. Underlying inflammation is an additional risk factor for the development of adynamic bone disease, which could explain the potential co-morbidity of this clinical condition.
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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 | 0.000 | 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) | 0.001 | 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".