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Record W2308309806 · doi:10.4172/2368-0512.1000057

PREdiction of ST deviations in lead aVR as a noninvasive tool to predict the infarct-Related coronary artery in patients with acute Inferior-wall Myocardial Infarction (The PREST-RIMI Study)

2016· article· en· W2308309806 on OpenAlexvenueno aff
Santosh Kumar Sinha, Vikas Mishra

Bibliographic record

VenueCurrent research. Cardiology · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyInternal medicineMyocardial infarctionCoronary artery diseaseRight coronary arteryElectrocardiographyST depressionST elevationLead (geology)Gold standard (test)ArteryCoronary angiographyST segment

Abstract

fetched live from OpenAlex

BACKgROunD: Acute myocardial infarction is one of the most common presentations of coronary artery disease (CAD). Although coronary angiography remains the gold standard for identification of the infarctrelated artery (IRA), conventional 12-lead electrocardiography (ECG) is an essential tool for diagnosis, risk stratification and prognosis. If specific ECG patterns can be recognized, it will be possible to determine the IRA and size of the ventricular area that is jeopardized. The existing ECG algorithms have good sensitivity for the right coronary artery (RCA) and good specificity for the left circumflex artery (LCx) as predictors of IRA in patients with acute inferior-wall myocardial infarction (IWMI), while the specificity for the RCA and sensitivity for the LCx are modest. OBjECTIVE: To evaluate deviations in lead aVR to predict IRA in patients with IWMI, and to validate several commonly used 12-lead ECG characteristics. METhODS: A total of 585 consecutive patients with a first occurrence of acute IWMI were analyzed for the association between ECG and IRA diagnosed using coronary angiography. Subsequently, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of depression in lead aVR, along with various commonly used ECG criteria for predicting the IRA, were estimated using coronary angiographic findings as the gold standard.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.323
Teacher spread0.287 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2016
Admission routes1
Has abstractyes

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