Variability in calcium, phosphorus, and parathyroid hormone in patients on hemodialysis
Bibliographic record
Abstract
Calcium, phosphorus, and parathyroid hormone (PTH) levels are routinely measured in patients undergoing chronic hemodialysis. Medications, diet, and dialytic therapies are modified based upon these lab values to achieve specific goal values in the hope of improving outcomes. However, the variability of these values in patients undergoing chronic hemodialysis has only been rarely studied. We prospectively investigated the variability of these measures in 35 patients undergoing chronic hemodialysis as well as the impact of this variability on clinical decision-making in a prospective manner over a month. There is significant session-to-session variability in phosphorus and PTH values (mean coefficient of variations [CV] of 0.19 and 0.31, respectively). Calcium variability is much lower (mean CV of 0.05). Not surprisingly, the CV for all values is increased during the long interdialytic interval. The impact of this variability on clinical decision-making was analyzed. The variability in calcium, phosphorus, and PTH values would lead to a different clinical decision in 23.6%, 41.2%, and 39.7% of different session lab values. We also investigated the variability of these lab measures over a year in these patients and found that the session-to-session variability was very similar to the month-to-month variability. The high degree of variability of these parameters has important implications for clinical decision-making and for implementation of pay-for-performance measures.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".