Associations between corrected serum calcium and phosphorus levels and outcome in dialysis patients in the Kumamoto Prefecture
Bibliographic record
Abstract
INTRODUCTION: Mortality in hemodialysis patients is relatively high; thus, its risk stratification is very important. There are insufficient data describing the current status of the management of serum phosphate and calcium levels. METHODS: We conducted a multicenter, prospective, registry study throughout the Kumamoto Prefecture in Japan. We enrolled 1993 patients at 58 facilities with complete explanatory data, including serum phosphate, corrected calcium, and intact parathyroid hormone levels. We categorized subjects into nine categories according to low, normal, and high levels of phosphate and corrected calcium levels. The endpoint was all-cause mortality. RESULTS: Of the total number of subjects, 56.1% of the patients were in the normal phosphate and calcium category, and 72% and 77.1% had controlled serum phosphate and calcium levels, respectively. Two hundred twenty-six deaths occurred during the follow-up period. In the nine categories, the highest mortality rates were observed in the highest corrected calcium and lowest phosphate categories. Stepwise backward multivariate regression analyses identified the serum corrected calcium level (OR, 1.38; 95% CI, 1.06-1.79; P = 0.016) and the serum phosphate level (OR, 1.26; 95% CI, 1.08-1.48; P = 0.003) as significant and independent predictors of all-cause mortality. CONCLUSIONS: The corrected serum calcium and phosphate levels are associated with mortality in our dialysis population, with poorest survival in patients with high corrected serum calcium and low serum phosphorus.
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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 | 0.001 | 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".