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Record W2946295359 · doi:10.1093/eurheartj/ehz301

Myocardial injury after non-cardiac surgery: diagnosis and management

2019· article· en· W2946295359 on OpenAlexaff
P.J. Devereaux, Wojciech Szczeklik

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsImpactMcMaster UniversityPopulation Health Research InstituteHamilton General Hospital
FundersAstraZeneca
KeywordsMedicineCardiac surgeryMedical emergencyIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

Myocardial injury after non-cardiac surgery (MINS) is due to myocardial ischaemia (i.e. supply-demand mismatch or thrombus) and is associated with an increased risk of mortality and major vascular complications at 30 days and up to 2 years after non-cardiac surgery. The diagnostic criteria for MINS includes an elevated post-operative troponin measurement judged as resulting from myocardial ischaemia (i.e. no evidence of a non-ischaemic aetiology), during or within 30 days after non-cardiac surgery, and without the requirement of an ischaemic feature (e.g. ischaemic symptom, ischaemic electrocardiography finding). For patients with MINS who are not at high risk of bleeding, physicians should consider initiating dabigatran 110 mg twice daily and low-dose aspirin. Physicians should also consider initiating statin therapy in patients with MINS. Most MINS patients should only be referred to cardiac catheterization if they demonstrate recurrent instability (e.g. cardiac ischaemia, heart failure). Patients ≥65 years of age or with known atherosclerotic disease should have troponin measurements on days 1, 2, and 3 after surgery while the patient is in hospital to avoid missing >90% of MINS and the opportunity to initiate secondary prophylactic measures and follow-up.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.001

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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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
GenreEmpirical

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

Citations203
Published2019
Admission routes1
Has abstractyes

Explore more

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