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Record W2277652879 · doi:10.1093/ndt/gfv183.51

FP733INTERNATIONAL TRENDS IN HEMODIAFILTRATION (HDF) USE IN THE DIALYSIS OUTCOMES AND PRACTICE PATTERNS STUDY (DOPPS)

2015· article· en· W2277652879 on OpenAlexaffabout
Angelo Karaboyas, Friedrich K. Port, Bruce Robinson, Tadashi Tomo, Jean Éthier, Raymond Vanholder, Francesca Tentori

Bibliographic record

VenueNephrology Dialysis Transplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineDialysisIntensive care medicineHemodialysisInternal medicine

Abstract

fetched live from OpenAlex

Introduction and Aims: Compared to hemodialysis (HD), hemodiafiltration (HDF) provides a higher clearance of a broad spectrum of uremic toxins, including middle molecules. This effect is largely proportional to the convective component, i.e. to the replacement fluid volume. While results of comparative studies of HDF vs HD have been mixed, recent randomized trials suggest that high replacement volume HDF may be associated with improved morbidity and mortality. These findings may have in part resulted in increased HDF use. The current study provides updated data on HDF use in the international Dialysis Outcomes and Practice Patterns Study (DOPPS) cohort over the last 15 years. Methods: We included 32,958 patients from DOPPS phases 1-5 (1998-2014) who had been on renal replacement therapy for > 90 days and dialyzing 3x/week at study entry. HDF at baseline was defined as volume replacement fluid ≥ 4 L, or (if missing) based on reported type of modality. United States and Canada were excluded because HDF use is very rare.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.341
Teacher spread0.286 · 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 designObservational
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
Published2015
Admission routes2
Has abstractyes

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