Bone‐specific alkaline phosphatase and bone turnover in African American hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: Noninvasive measures of bone activity include intact parathyroid hormone (iPTH) and bone-specific alkaline phosphatase (BSAP). Whether BSAP measurement alone or in combination with other biochemical data provides more reliable information about bone turnover than iPTH alone in African Americans on hemodialysis is unknown. METHODS: This cross-sectional study aimed to determine the optimal predictor and cutoff points for BSAP, iPTH, calcium and phosphorus in classifying bone biopsy findings. Forty-three African American hemodialysis patients were available for analysis. Biochemical data on the day of biopsy across a spectrum of qualitative histologic bone features were compared. Classification and regression tree analysis was used to determine both the optimal predictor and cutoff points for BSAP, iPTH, calcium and phosphorus in identifying bone turnover status. FINDINGS: Seven subjects had adynamic disease, 31 had mild/moderate hyperparathyroid bone features, and five had severe hyperparathyroid bone disease. BSAP was the optimal predictor of bone biopsy with a cutoff point of 22 ng/mL. Calcium and phosphorus had no predictive value. At BSAP ≤ 22 ng/mL, subjects had either adynamic bone disease or mild/moderate hyperparathyroid bone disease but iPTH was not useful in further classifying biopsy findings. When BSAP was >22 ng/mL, subjects had either mild/moderate or severe hyperparathyroid bone disease, and iPTH was useful in further classifying biopsy findings. With BSAP > 22 ng/mL and iPTH < 726 pg/mL, all subjects had mild/moderate bone turnover features. DISCUSSION: Compared to iPTH, BSAP was shown to be the optimal predictor of biopsy findings with an optimal cutoff at 22 ng/mL.
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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 | 0.002 |
| 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) | 0.002 | 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".