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Record W38676804 · doi:10.1177/039139880703000503

Improving Outcomes from Acute Kidney Injury (AKI): Report on an Initiative

2007· article· en· W38676804 on OpenAlexaff
Claudio Ronco, Adeera Levin, David G. Warnock, Ravi Mehta, John A. Kellum, Sudhir V. Shah, Bruce A. Molitoris, Arvind Bagga, Ayşı̇n Bakkaloğlu, Joseph V. Bonventre, Emmanuel A. Burdmann, Yipu Chen, Prasad Devarajan, V. D�Intini, Geoff Dobb, Charles G. Durbin, Kai‐Uwe Eckardt, Claude Guérin, Stefan Herget‐Rosenthal, Eric A. J. Hoste, Michael Joannidis, Ashok Kirpalani, Andrea Lassnigg, Jean‐Roger Le Gall, Raúl Lombardi, William L. Macias, Constantine A. Manthous, Ravindra L. Mehta, Miet Schetz, Frédérique Schortgen, Patrick SK Tan, Haiyan Wang, David G. Warnock, Steve Webb

Bibliographic record

VenueThe International Journal of Artificial Organs · 2007
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsAcute kidney injuryMultidisciplinary approachMedicineIntensive care medicineNephrologyRenal replacement therapyMedical emergencyAcute careMEDLINEInternal medicineHealth carePolitical science

Abstract

fetched live from OpenAlex

Acute Kidney Injury (AKI) is a complex disorder for which currently there is no accepted definition. We describe an initiative to develop uniform standards for defining and classifying AKI and establish a forum for multidisciplinary interaction to improve care for patients with, or at risk for AKI. Members representing key societies in critical care and nephrology along with additional experts in adult and pediatric AKI participated in a 2-day conference in Amsterdam in September 2005 to draft consensus recommendations for diagnosing and staging AKI. This report describes the proposed diagnostic and staging criteria for AKI and the formation of a multidisciplinary collaborative network.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.388
Teacher spread0.342 · 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 designBench or experimental
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

Citations56
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueThe International Journal of Artificial OrgansSame topicAcute Kidney Injury ResearchFrench-language works237,207