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Record W4212961746 · doi:10.3410/f.1108801.566151

Faculty Opinions recommendation of Intensity of renal support in critically ill patients with acute kidney injury.

2008· dataset· en· W4212961746 on OpenAlexfundno aff
Greg S. Martin

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2008
Typedataset
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesVeterans Affairs San Diego Healthcare SystemClinical Science Research and DevelopmentCenters for Medicare and Medicaid ServicesUniversity of North Carolina at Chapel HillUniversity of Texas MD Anderson Cancer CenterNational Institutes of HealthOffice of Research and DevelopmentU.S. Department of Veterans AffairsUniversity of TorontoMassachusetts General HospitalCase Western Reserve UniversityCleveland ClinicCleveland Clinic FoundationVA Pittsburgh Healthcare SystemWashington University in St. LouisUniversity of PennsylvaniaMedical Center, University of PittsburghGenentechNxStageUniversity of PittsburghAmgenJohns Hopkins UniversityUniversity of California, San FranciscoWake Forest UniversityUniversity of Miami
KeywordsCritically illAcute kidney injuryMedicineRenal injuryIntensive care medicineIntensity (physics)Medical emergencyEmergency medicineKidneyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND-The optimal intensity of renal-replacement therapy in critically ill patients with acute kidney injury is controversial.METHODS-We randomly assigned critically ill patients with acute kidney injury and failure of at least one nonrenal organ or sepsis to receive intensive or less intensive renal-replacement therapy.The primary end point was death from any cause by day 60.In both study groups, hemodynamically stable patients underwent intermittent hemodialysis, and hemodynamically unstable patients underwent continuous venovenous hemodiafiltration or sustained low-efficiency dialysis.Patients receiving the intensive treatment strategy underwent intermittent hemodialysis and sustained lowefficiency dialysis six times per week and continuous venovenous hemodiafiltration at 35 ml per kilogram of body weight per hour; for patients receiving the less-intensive treatment strategy, the corresponding treatments were provided thrice weekly and at 20 ml per kilogram per hour.RESULTS-Baseline characteristics of the 1124 patients in the two groups were similar.The rate of death from any cause by day 60 was 53.6% with intensive therapy and 51.5% with less-intensive therapy (odds ratio, 1.09; 95% confidence interval, 0.86 to 1.40; P = 0.47).There was no significant difference between the two groups in the duration of renalreplacement therapy or the rate of recovery of kidney function or nonrenal organ failure.Hypotension during intermittent dialysis occurred in more patients randomly assigned to receive intensive therapy, although the frequency of hemodialysis sessions complicated by hypotension was similar in the two groups.CONCLUSIONS-Intensive renal support in critically ill patients with acute kidney injury did not decrease mortality, improve recovery of kidney function, or reduce the rate of nonrenal organ failure as compared with less-intensive therapy involving a defined dose of intermittent hemodialysis three times per week and continuous renal-replacement therapy at 20 ml per kilogram per hour.(ClinicalTrials.govnumber, NCT00076219.)Acute kidney injury is a common complication of acute illness, affecting approximately 2 to 7% of hospitalized patients 1-4 and more than 35% of critically ill patients.5-8 Renalreplacement therapy is the mainstay of supportive treatment of patients with severe acute kidney injury; its use is required in 5 to 6% of critically ill patients and is associated with inhospital mortality rates of 50 to 80%.5,9-12

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)0.002
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.998
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0700.025

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 head0.027
GPT teacher head0.368
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.

Study designNot applicable
DomainEvaluation
GenreDataset

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
Published2008
Admission routes1
Has abstractyes

Explore more

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