Achieving targets for bone and mineral metabolism: The impact of cinacalcet HCl in clinical practice
Bibliographic record
Abstract
Achieving the K/DOQI targets for bone and mineral metabolism has proven difficult with the use of vitamin D analogues and phosphate binders. The introduction of cinacalcet HCl provided a new tool with a novel therapeutic mechanism of action. The purpose of this study was to evaluate the effect of the introduction of combination algorithm for managing secondary hyperparathyroidism (SHPT) on phosphorus, calcium, and biointact parathyroid hormone (PTH). The 61 patients who dialyzed in the facility from January 2004 (baseline) and who remained in the facility as of April 2005 (follow-up) were included in the study. In the baseline period, 37 (61%) of the patients received paricalcitol at some time during the 3-month observation period. In the follow-up period, 19% or 31% of the patients received cinacalcet HCl. Of those not receiving cinacalcet HCl, 67% had PTH at or below target, 17% were felt to be noncompliant with oral meds, 7% had low calcium, and 10% either could not get the medication or were not switched to the combination pathway. Compared with the baseline period, the percent of patients who met the PTH target increased from 19.7% to 37.7%, p<0.05. The percent of patients meeting all 4 targets increased from 14.8% to 24.6%, although this did not reach statistical significance. The introduction of cinacalcet HCl into a treatment algorithm for management of SHPT resulted in a significant increase in the percentage of patients achieving the PTH target while maintaining the other mineral metabolism targets.
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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 | 0.004 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".