Mineral metabolism management in Canadian peritoneal dialysis patients
Bibliographic record
Abstract
BACKGROUND: Abnormal mineral metabolism is associated with increased morbidity and mortality in dialysis patients. Therefore, the goal of this study was to compare a) mineral metabolism control among a cohort of Canadian peritoneal dialysis (PD) patients to K/DOQI-defined targets and b) the effect of different treatment strategies on mineral metabolism parameters. METHODS: We looked at a cohort of 317 Canadian PD patients from 9 clinics that used the PhotoGraph™ software program which tracks mineral metabolism management. Serum phosphorus (P), calcium (Ca) and intact parathyroid hormone (iPTH) values were collected for the patients. Data were categorized and analyzed by the type of phosphate binder prescribed, vitamin D use, and dosing and reimbursement criteria for the phosphate binder, sevelamer. RESULTS: The majority of patients achieved K/DOQI-set targets for serum P. Patients who resided in Quebec (QC), which had greater access to sevelamer, had lower mean concentrations of P and Ca, were less likely to take Ca-based phosphate binders (CBBs) exclusively and were exposed to less exogenous Ca than in Ontario (ON). CONCLUSION: Availability of the phosphate binder sevelamer and reduced doses of elemental Ca were associated with more mineral metabolism parameters within suggested target ranges. Further studies that focus on patient outcomes are warranted.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".