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EARLY INITIATION OF PHOSPHATE LOWERING DIETARY THERAPY IN NON‐DIALYSIS CHRONIC KIDNEY DISEASE: A CRITICAL REVIEW

2009· review· en· W2161229478 on OpenAlexaff
Mhairi K. Sigrist, Giusy Chiarelli, Linda Lay Hoon Lim, Adeera Levin

Bibliographic record

VenueJournal of Renal Care · 2009
Typereview
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsSt. Paul's HospitalUniversity of British ColumbiaProvidence Health Care
Fundersnot available
KeywordsHyperphosphatemiaMedicinePhosphateKidney diseasePhosphate binderPopulationDialysisSecondary hyperparathyroidismIntensive care medicineInternal medicineDiseaseEndocrinologyParathyroid hormoneEnvironmental healthBiochemistryCalciumBiology

Abstract

fetched live from OpenAlex

Dietary management of hyperphosphatemia and hyperparathyroidism have long been important elements in the clinical management of CKD stage 4 and 5 for the prevention of mineral bone disease. The rationale for phosphate lowering has been further justified, given the accumulating data to support the association of phosphate with vascular damage, in this population who are at high risk of cardiovascular (CV) death. Phosphate is a novel CV risk factor in both CKD and in the general population, and a growing body of literature suggests that high normal serum phosphate may be a risk factor for progression of CKD. Few studies have examined hard outcomes after phosphate lowering. Nonetheless, given the balance of data both in cell, animal and human studies, the use of phosphate lowering strategies at earlier stages of CKD, perhaps even prior to serum phosphate level rising, may well be justified. This review will discuss the complications associated with higher serum phosphate, the potential benefits of early phosphate intervention, practical considerations of low phosphate diets and novel strategies for evaluating these strategies in clinical practice.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.041
GPT teacher head0.371
Teacher spread0.331 · 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 designOther design
Domainnot available
GenreReview

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

Citations19
Published2009
Admission routes1
Has abstractyes

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