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The influence of clinical risk factors on pre‐operative B‐type natriuretic peptide risk stratification of vascular surgical patients

2011· review· en· W2159991294 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 · 2011
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineNatriuretic peptideRisk stratificationInternal medicineRisk factorRisk assessmentCardiologyHeart failure

Abstract

fetched live from OpenAlex

The role of the revised cardiac risk index in risk stratification has recently been challenged by studies reporting on the superior predictive ability of pre-operative B-type natriuretic peptides. We found that in 850 vascular surgical patients initially risk stratified using B-type natriuretic peptides, reclassification with the number of revised cardiac risk index risk factors worsened risk stratification (p < 0.05 for > 0, > 2, > 3 and > 4 risk factors, and p = 0.23 for > 1 risk factor). When evaluated with pre-operative B-type natriuretic peptides, none of the revised cardiac risk index risk factors were independent predictors of major adverse cardiac events in vascular patients. The only independent predictor was B-type natriuretic peptide stratification (OR 5.1, 95% CI 1.8-15 for the intermediate class, and OR 25, 95% CI 8.7-70 for the high-risk class). The clinical risk factors in the revised cardiac risk index cannot improve a risk stratification model based on B-type natriuretic peptides.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.663
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.043
GPT teacher head0.363
Teacher spread0.320 · 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