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Management of hyperphosphatemia

2006· review· en· W2033724267 on OpenAlexvenueno aff
Martin K. Kuhlmann

Bibliographic record

VenueHemodialysis International · 2006
Typereview
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHyperphosphatemiaMedicinePhosphate binderPhosphateHemodialysisDialysisCalorieIntensive care medicineMalnutritionInternal medicinePhosphorusEndocrinologyKidney diseaseBiochemistryChemistry

Abstract

fetched live from OpenAlex

Hyperphosphatemia is a well recognized risk factor for cardiovascular mortality in dialysis patients. Despite advanced technology and regular and efficient dialysis treatment the prevalence of hyperphosphatemia is still high. The goal of normalization of serum phosphorus (iP) levels can only be reached by optimization of dialysis prescription in combination with individualized dietary and medical strategies. Due to the unique characteristics of intradialytic iP kinetics, dialysis treatment time and frequency are the most effective factors governing intradialytic iP removal. Although the combination of diffusive and convective removal by hemodiafiltration allows a further increase in iP mass removal, a neutral phosphorus balance without dietary restrictions and the use of phosphate binders has only be achieved by daily nocturnal hemodialysis. Strict dietary phosphate restriction bears the risk of inadequate protein intake and the development of protein/calorie malnutrition. Although phosphate binders (PB) can effectively lower serum iP levels into the normal range, this is rarely achieved in clinical practice probably due to inadequate relation of PB dose to dietary phosphorus intake. Developing methods to enable patients to self-adjust phosphate binder dose to individual meal phosphate content, similar to adjusting insulin dose to carbohydrate intake, may lead to further improvements in phosphate management.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.034
GPT teacher head0.350
Teacher spread0.317 · 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 designNot applicable
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

Citations48
Published2006
Admission routes1
Has abstractyes

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