PTH suppression by calcitriol does not predict off‐target actions in experimental CKD
Bibliographic record
Abstract
) is a former first-line VDRA in CKD treatment. However, a consequence of its use in CKD is accelerated vascular calcification (VC). An experimental CKD model was used to determine whether altering the calcitriol delivery profile to obtain different PTH suppression levels could improve vascular health outcomes. High adenine diet (0.25%) was used to generate experimental CKD in rats. CKD rats were treated using different calcitriol dosing strategies: (a) 20 ng/kg SD (n = 8), (b) 80 ng/kg SD (n = 8), (c) 5 ng/kg QID (n = 9), or (d) 20 ng/kg QID (n = 9). Multiple targets of calcitriol were assessed which include arterial calcium and phosphate as well as circulating calcium, phosphate, PTH, FGF-23, VWF, and vitamin D metabolome. PTH suppression occurred dose-dependently after 1-week calcitriol treatment (P < .01), but the suppressive effect was lost over time. Both VC and circulating FGF-23 increased > 10× in all calcitriol-treated rats (P < .05 and P < .001, respectively); similarly, circulating VWF increased at all time points (P < .05). Ad-hoc analysis of CKD morbidities in treated rats indicated no differences in negative outcomes based on PTH suppression level (minimal-, target-, and over-). Comparing different calcitriol dosing strategies revealed the following: (a) despite initial calcitriol-influenced PTH suppression across all treatments, the ability to continually suppress PTH was markedly reduced by study conclusion and (b) PTH suppression level is not an adequate proxy for improvements in overall CKD morbidity. These findings show (a) a more holistic approach to evaluate CKD treatment efficacy aside from PTH suppression is needed and (b) that other VDRA therapies should be examined in CKD treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".