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Record W768113253

RENAL FUNCTION IN PATIENTS UNDERGOING SURGERY

2014· dissertation· en· W768113253 on OpenAlexfundno aff
Michael Walsh

Bibliographic record

VenueMacSphere (McMaster University) · 2014
Typedissertation
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteKidney Foundation of CanadaCanadian Institutes of Health ResearchCanadian Society of NephrologyHeart and Stroke Foundation of Canada
KeywordsMedicineRenal functionAcute kidney injuryCardiac surgeryTroponinRandomized controlled trialKidneySurgeryCardiologyInternal medicineMyocardial infarction
DOInot available

Abstract

fetched live from OpenAlex

Reduced kidney function around the time of surgery is an important risk factor for postoperative mortality. Despite this there is limited information on how reduced kidney function prior to surgery alters prognosis, what causes sudden decrements in kidney function after surgery (known as acute kidney injury), or how they might be avoided. The studies in this thesis inform these knowledge gaps. Chapter 2 describes the results of a post hoc analysis of the interaction between preoperative estimated glomerular filtration rate, a marker of kidney function, and postoperative cardiac troponin T, a marker of heart damage, for predicting 30-day mortality in a prospective cohort study of patients undergoing noncardiac surgery. Chapter 3 uses administrative and clinical data from a single centre to inform the risk of acute kidney injury after noncardiac surgery by concentrations of preoperative hemoglobin and change in postoperative hemoglobin. Chapter 4 uses the same data to determine a definition of intraoperative hypotension that is prognostic of acute kidney injury, myocardial injury and death. Chapter 5 describes a randomized controlled trial that compares a novel therapeutic procedure called remote ischemic preconditioning to a sham procedure in patients undergoing cardiac surgery.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.010
GPT teacher head0.198
Teacher spread0.188 · 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.

Study designObservational
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

Explore more

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