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Record W3154243593 · doi:10.1016/j.ekir.2021.03.603

POS-574 OPTIMIZATION OF FUROSEMIDE USE IN HEMODIALYSIS PATIENTS

2021· article· en· W3154243593 on OpenAlexaff
Mérilie Gagnon, Corinna Weber

Bibliographic record

VenueKidney International Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineFurosemideHemodialysisDiureticWeight gainQuality of life (healthcare)Renal functionInternal medicineIntensive care medicineCardiologyBody weight

Abstract

fetched live from OpenAlex

Residual renal function (RRF) in hemodialysis (HD) patients has been the focus of multiple studies evaluating its impact on patient outcomes and relationship with the use of diuretics. The CHOICE study showed self-reported RRF was associated with better survival and quality of life, and a DOPPS study demonstrated the use of diuretics was associated with lower interdialytic weight gain.1,2 Similar findings were reported by Sibbel et al. in patients using furosemide after initiation of HD with lower rates of hospitalization, intradialytic hypotension and lower interdialytic weight gain compared to those who did not use diuretics3.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.019
GPT teacher head0.296
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2021
Admission routes1
Has abstractyes

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