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Record W2415397808 · doi:10.1177/1089253215593177

Acute Kidney Injury in Cardiac Surgery and Cardiac Intensive Care

2015· review· en· W2415397808 on OpenAlex
Gary Lau, Ron Wald, Robert N. Sladen, C. David Mazer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSeminars in Cardiothoracic and Vascular Anesthesia · 2015
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineAcute kidney injuryCardiac surgeryRenal replacement therapyIncidence (geometry)Intensive care medicineComplicationModalitiesPathophysiologyInternal medicineCardiology

Abstract

fetched live from OpenAlex

Acute kidney injury (AKI) is a serious postoperative complication following cardiac surgery. Despite the incidence of AKI requiring temporary renal replacement therapy being low, it is nonetheless associated with high morbidity and mortality. Therefore, preventing AKI associated with cardiac surgery can dramatically improve outcomes in these patients. The pathogenesis of AKI is multifactorial and many attempts to prevent or treat renal injury have been met with limited success. In this article, we will discuss the incidence and risk factors for cardiac surgery associated AKI, including the pathophysiology, potential biomarkers of injury, and treatment modalities.

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.002
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.037
GPT teacher head0.372
Teacher spread0.335 · 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