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Record W2105114108 · doi:10.1159/000349962

Implementation of Novel Biomarkers in the Diagnosis, Prognosis, and Management of Acute Kidney Injury: Executive Summary from the Tenth Consensus Conference of the Acute Dialysis Quality Initiative (ADQI)

2013· article· en· W2105114108 on OpenAlexaff
Peter A. McCullough, Josée Bouchard, Sushrut S. Waikar, Edward D. Siew, Zoltán Endre, Stuart L. Goldstein, Jay L. Koyner, Etienne Macedo, Kent Doi, Salvatore Di Somma, Andrew Lewington, Ravi Thadhani, Raj Chakravarthi, Can Ice, Mark D. Okusa, Jacques Duranteau, Peter Doran, Yang Li, Bertrand L. Jaber, S. Meehan, John A. Kellum, Michael Haase, Patrick Murray, Dinna N. Cruz, Alan S. Maisel, Sean M. Bagshaw, Lakhmir S. Chawla, Ravindra L. Mehta, Andrew Shaw, Claudio Ronco

Bibliographic record

VenueContributions to nephrology · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of AlbertaUniversité de MontréalMontfort Hospital
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesAstute MedicalAlere
KeywordsMedicineIntensive care medicineAcute kidney injuryDialysisBiomarkerKidney diseaseInternal medicine

Abstract

fetched live from OpenAlex

Detection of acute kidney injury is undergoing a dynamic revolution of biomarker technology allowing greater, earlier, and more accurate determination of diagnosis, prognosis, and with powerful implication for management. Biomarkers can be broadly considered as any measurable biologic entity or process that allows differentiation between normal function and injury or disease. The ADQI (Acute Dialysis Quality Initiative) had its Ninth Consensus Conference dedicated to synthesis and formulation of the existing literature on biomarkers for the detection of acute kidney injury in a variety of settings. In the papers that accompany this summary, ADQI workgroups fully develop key concepts from a summary of the literature in the domains of early diagnosis, differential diagnosis, prognosis and management, and concurrent physiologic and imaging measures.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.347
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.385
Teacher spread0.337 · 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 teacher head, 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

Citations117
Published2013
Admission routes1
Has abstractyes

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