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Record W2063356426 · doi:10.1111/sdi.12300

Unnecessary Renal Replacement Therapy for Acute Kidney Injury is Harmful for Renal Recovery

2014· editorial· en· W2063356426 on OpenAlexafffund
Edward G. Clark, Sean M. Bagshaw

Bibliographic record

VenueSeminars in Dialysis · 2014
Typeeditorial
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of AlbertaOttawa Hospital
FundersCanada Research ChairsAlberta Innovates - Health Solutions
KeywordsMedicineRenal replacement therapyAcute kidney injuryIntensive care medicineKidney diseaseDialysisHemodialysisModalitiesLimitingDiseaseHarmInternal medicine

Abstract

fetched live from OpenAlex

The use of renal replacement therapy (RRT) for severe acute kidney injury (AKI) is frequently necessary in the face of life-threatening complications; however, there is wide practice variation with respect to triggers for RRT initiation. Recent evidence suggests that RRT may be independently associated with impaired recovery following AKI. There are plausible mechanistic reasons why RRT may be harmful and this concept is supported by ancillary evidence in the form of studies that have assessed the impact of different modalities of RRT for AKI as well as some of the literature pertaining to initiation of chronic hemodialysis in end-stage kidney disease patients (ESKD). As such, avoiding unnecessary RRT (URRT) is a desirable goal. There is emerging evidence of strategies that may be effective to help limit URRT. These strategies primarily involve early identification of AKI and limiting iatrogenic harm once AKI is established. Further research into defining and preventing URRT may help improve the consistently poor outcomes following severe AKI with respect to development of chronic kidney disease and ESKD.

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)0.008
metaresearch head score (Gemma)0.029
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.018
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0060.005
Open science0.0050.002
Research integrity0.0180.030
Insufficient payload (model declined to judge)0.0050.006

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 head0.015
GPT teacher head0.357
Teacher spread0.341 · 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
GenreEditorial

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

Citations52
Published2014
Admission routes2
Has abstractyes

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