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Record W2098774999 · doi:10.1093/ndtplus/sfp167

The effects of discontinuing cinacalcet at the time of kidney transplantation

2009· article· en· W2098774999 on OpenAlexaff
M. Jadoul, Ana Baños, Valter J. Zani, Gavril Hercz

Bibliographic record

VenueClinical Kidney Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsHumber River Regional Hospital
FundersAmgen
KeywordsCinacalcetMedicineCalcimimeticSecondary hyperparathyroidismKidney transplantationDialysisUrologyTransplantationKidney diseaseHyperparathyroidismInternal medicineParathyroid hormoneCalcium

Abstract

fetched live from OpenAlex

Background. The calcimimetic, cinacalcet, is approved for treating secondary hyperparathyroidism (SHPT) in patients with chronic kidney disease (CKD) on dialysis. Biochemical profiles and clinical outcomes in patients discontinuing cinacalcet at kidney transplantation have not been previously described.Methods. We performed a retrospective observational study evaluating post-transplant biochemical profiles and clinical outcomes in patients who had enrolled in phase 2 or 3 randomized, placebo-controlled studies of cinacalcet before receiving a kidney transplant.Results. The study included 28 former cinacalcet and 10 former placebo patients. Post-kidney transplant, there were no obvious differences between the two groups in levels of serum intact parathyroid hormone, calcium or phosphorus. One patient in each group underwent post-transplant parathyroidectomy. Kidney transplant failure was apparent in one former cinacalcet-treated patient (4%) and three former placebo patients (30%). The duration of hospitalization (mean +/- standard error) immediately post-transplant in these two groups was 2.3 +/- 0.3 and 3.4 +/- 0.8 weeks, respectively.Conclusions. Using cinacalcet to treat SHPT in patients with CKD awaiting kidney transplantation does not appear to modify SHPT-related post-transplant biochemical profiles, or clinical outcomes, compared with placebo.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.334
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2009
Admission routes1
Has abstractyes

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