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Record W2078988527 · doi:10.1159/000224788

Renal Replacement Therapy for Acute Kidney Injury

2009· review· en· W2078988527 on OpenAlexaff
Heather Fieghen, Ron Wald, Bertrand L. Jaber

Bibliographic record

VenueNephron Clinical Practice · 2009
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsRenal replacement therapyMedicineAcute kidney injuryNephrologyIntensive care medicineHemodialysisKidneyKidney diseaseInternal medicine

Abstract

fetched live from OpenAlex

The treatment of established acute kidney injury (AKI) is largely supportive in nature. Renal replacement therapy remains the cornerstone of management for the minority of patients who have severe AKI. Optimization of renal replacement therapy may modulate the high mortality associated with AKI. Recent trials indicated that continuous renal replacement therapy does not confer a survival advantage as compared to intermittent hemodialysis. Furthermore, there is no evidence to support a more intensive strategy of renal replacement therapy in the setting of AKI. There is comparatively limited data regarding the ideal timing of renal replacement therapy initiation and the preferred mode of solute clearance.

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: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.206
GPT teacher head0.582
Teacher spread0.376 · 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
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

Citations33
Published2009
Admission routes1
Has abstractyes

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