The Impact of Prior Multidisciplinary Predialysis Care on Mineral Metabolic Control among Chronic Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND/AIMS: Disordered mineral metabolism is independently associated with mortality among chronic dialysis patients. We hypothesized that, upon dialysis start, biochemical markers of mineral metabolism would be better controlled among patients who had received multidisciplinary predialysis care (MDC). METHODS: We conducted a retrospective cohort study of incident hemodialysis patients between 2002 and 2005. Corrected calcium (Ca), phosphate (P), calcium-phosphate product (CaxP), and intact parathyroid hormone (iPTH) at the time of dialysis initiation and over the first year thereafter were compared based on prior MDC receipt. Furthermore, we examined the relationship between the duration of MDC and mineral metabolic parameters. RESULTS: 67 patients received MDC and 84 patients received conventional or no nephrologist-based care. Patients who received MDC had a higher iPTH (p = 0.03) both at dialysis initiation and over the subsequent year while Ca, P, and CaxP were not significantly impacted. Among patients who received MDC, mineral metabolic values at dialysis initiation did not differ by duration of predialysis care. CONCLUSIONS: The receipt of MDC had a limited effect on mineral metabolic profiles at the time of and over the first year following chronic hemodialysis initiation. The survival benefits associated with the receipt of MDC may be mediated by mechanisms other than improved mineral metabolic control.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".