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Record W2106171307 · doi:10.1111/anae.12635

Cardiac biomarkers in the prediction of risk in the non‐cardiac surgery setting

2014· review· en· W2106171307 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnaesthesia · 2014
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineNatriuretic peptideRisk stratificationTroponinCardiac surgeryInternal medicineBiomarkerCardiologyRisk assessmentTroponin IClinical trialBrain natriuretic peptideTroponin TIntensive care medicineHeart failure

Abstract

fetched live from OpenAlex

B-Type natriuretic peptides and troponin measurements have potential in predicting risk in patients undergoing non-cardiac surgery. Using the American Heart Association framework for the evaluation of novel biomarkers, we review the current evidence supporting the peri-operative use of these two biomarkers. In patients having major non-cardiac surgery who are risk stratified using clinical risk scores, the measurement of natriuretic peptides and troponin, both before and after surgery, significantly improves risk stratification. However, only pre- and postoperative natriuretic peptide measurement and postoperative troponin measurement have shown clinical utility. It is now important for trials to be conducted to determine whether integrating pre- and postoperative natriuretic peptide and postoperative troponin measurement into clinical practice is able to improve clinical outcomes in patients undergoing non-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.

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)0.009
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.288
Teacher spread0.264 · 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