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Record W1511704073 · doi:10.5772/37434

Acute Kidney Injury Following Cardiac Surgery: Prevention, Diagnosis, and Management

2012· book-chapter· en· W1511704073 on OpenAlex

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

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsHôpital du Sacré-Cœur de MontréalUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineAcute kidney injuryCardiac surgeryIntensive care medicineCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Acute kidney injury (AKI) following cardiac surgery is associated with increased morbidity and mortality, longer hospital stays, and significantly increased health care costs. The physiological functions performed by the kidney, which include acid-base control, blood pressure regulation, water balance, and waste excretion, are crucial to the maintenance of homeostasis and can only partially be accomplished using renal replacement therapy (RRT). A number of risk factors have been identified that should be recognized in order to counsel patients appropriately and attempt to prevent AKI. Several pharmacologic and therapeutic modalities have been suggested, with varying levels of evidence, to aid in prevention of AKI and limit the extent of injury and morbidity once renal dysfunction has been recognized. The purpose of this chapter is to review the epidemiology, prevention, diagnosis, and treatment of acute kidney injury following cardiac surgery. These topics will be reviewed in detail in the discussion that follows.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.226
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.324
Teacher spread0.288 · 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