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Record W2606722411 · doi:10.1111/hdi.12559

Survival comparisons of intensive vs. conventional hemodialysis: Pitfalls and lessons

2017· review· en· W2606722411 on OpenAlexaffvenue
Amanda J. Miller, Jeff Perl, Karthik Tennankore

Bibliographic record

VenueHemodialysis International · 2017
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of TorontoNova Scotia Health Authority
Fundersnot available
KeywordsMedicineHemodialysisDialysisIntensive care medicineGeneralizability theoryObservational studyIntensive careMedical prescriptionInternal medicinePharmacology

Abstract

fetched live from OpenAlex

The optimal dose of hemodialysis (HD) has not yet been established. As a means of better approximating the physiology of native kidney function, there has been a growing interest in intensive HD (an increase in dialysis frequency and/or duration). Although many studies have demonstrated a survival benefit with intensive dialysis, results have been conflicting. This controversy stems from the challenges of randomizing patients to conventional vs. intensive HD modalities and, therefore, the reliance on observational comparisons that have been limited by varying definitions for intensive dialysis, differences in dialysis location and prescription, unavoidable treatment selection bias, and a potential lack of generalizability. This review will discuss the pitfalls and complexities surrounding survival comparisons with intensive HD, and identify important directions for future study.

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)0.045
metaresearch head score (Gemma)0.106
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.045
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.106
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0030.005
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0030.002
Research integrity0.0020.004
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 head0.111
GPT teacher head0.393
Teacher spread0.281 · 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 designSystematic review
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

Citations17
Published2017
Admission routes2
Has abstractyes

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