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Record W4366769041 · doi:10.1093/ckj/sfad089

Pharmacological strategies to manage hyperkalaemia: out with the old, in with the new? Not so fast…

2023· letter· en· W4366769041 on OpenAlexaff
Juan Jesús Carrero, Manish M. Sood, Ailema González-Ortíz, Catherine M. Clase

Bibliographic record

VenueClinical Kidney Journal · 2023
Typeletter
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsMcMaster UniversityImpactOttawa Hospital
FundersVetenskapsrådetConsejo Nacional de Ciencia y Tecnología
KeywordsScrutinySAFERIntensive care medicineMedicineDrugClinical trialDrug approvalRisk analysis (engineering)BusinessPharmacologyInternal medicineComputer scienceComputer securityPolitical science

Abstract

fetched live from OpenAlex

Since the 1950s, sodium polystyrene sulphonate (SPS) has been the dominant cation exchange agent prescribed for hyperkalaemia. Clinicians have had plenty of time to learn of SPS's advantages and limitations. The demands of drug regulatory agencies regarding the incorporation of medications into the market were not so stringent then as they are today, and the efficacy and safety of SPS have been questioned. In recent years, two novel cation exchangers, patiromer and sodium zirconium cyclosilicate, have received (or are in the process of receiving) regulatory approval in multiple jurisdictions globally, after scrutiny of carefully conducted trials regarding their short-term and mid-term efficacy. In this debate, we defend the view that all three agents are likely to have similar efficacy. Harms are much better understood for SPS than for newer agents, but currently there are no data to suggest that novel agents are safer than SPS. Drug choices need to consider costs, access and numbers-needed-to-treat to prevent clinically important events; for potassium exchangers, we need trials directly examining clinically important events.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.032
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.016
Insufficient payload (model declined to judge)0.0000.001

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 head0.051
GPT teacher head0.361
Teacher spread0.310 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2023
Admission routes1
Has abstractyes

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