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Record W2345377807 · doi:10.1111/hdi.12422

Optimizing dialysate potassium

2016· article· en· W2345377807 on OpenAlexaffvenue
Jennifer Lee, David C. Mendelssohn

Bibliographic record

VenueHemodialysis International · 2016
Typearticle
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsUniversity of TorontoHumber River Regional Hospital
Fundersnot available
KeywordsMedicinePotassiumHemodialysisHyperkalemiaInternal medicineMetallurgy

Abstract

fetched live from OpenAlex

Potassium shifts in thrice weekly HD patients are likely a reversible cause of arrhythmia and sudden cardiac death. In general, a dialysate potassium <2.0 mmol/L should be avoided, and many patients with dialysate potassium of 2 mmol/L could safely be adjusted upwards. The ideal predialysis serum potassium should be around 5.0 mmol/L. Trends in serum potassium and not single values, should inform chronic changes of dialysate potassium prescription. Atypical values should be dealt with as a one off, but should not lead to chronic bath changes. Referral to a renal dietician for counseling to limit dietary potassium intake is vital to prevent recurrence of these atypical episodes. Finally, facilities should develop and implement a formal and reliable way to alert the physician about possible potassium bath mismatching. This facility level approach works best if a policy is developed and endorsed by all involved stakeholders.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.001

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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
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

Citations7
Published2016
Admission routes2
Has abstractyes

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