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Record W2165387095 · doi:10.1093/ejechocard/jen137

Investigating the European Society of Cardiology Diastology Guidelines in a practical scenario

2008· article· en· W2165387095 on OpenAlexaff
W. T. Emery, I. Jadavji, Jonathan Choy, Richard Lawrance

Bibliographic record

VenueEuropean Journal of Echocardiography · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta Hospital
Fundersnot available
KeywordsMedicineCardiologyInternal medicineHeart failure with preserved ejection fractionEjection fractionDiastoleDoppler echocardiographyHeart failureBlood pressure

Abstract

fetched live from OpenAlex

AIMS: Recently, the European Society of Cardiology (ESC) released a consensus statement for the diagnosis of heart failure with preserved ejection fraction (HFPEF). It state that E/e' > 15 or <8 clearly define those with or without HFPEF and that for those in the range 8-15, other parameters should be examined. METHODS AND RESULTS: We retrospectively analysed 1229 consecutive echocardiograms (57% males) for the utility of echocardiographic measures including left atrial volume index (LAVI), left ventricular mass index (LVMI), and pulmonary venous and mitral inflow Doppler. LAVI of 40 ml/m(2) provided the greatest sensitivity and specificity of 76 and 77%, respectively, with reference to E/e' for the detection of diastolic dysfunction. The ESC definition of raised LVMI yielded a sensitivity and specificity of 32 and 99%, respectively. We found that the mitral and pulmonary inflow provided little incremental information. These results remained consistent between those with normal and abnormal ejection fraction. CONCLUSIONS: There appears to be little incremental value of pulmonary and mitral Doppler measures beyond the measure of mitral E wave. An LAVI cut-off of 40 ml/m(2) maximizes both sensitivity and specificity. However, ESC guidelines of raised LVMI in patients with HFPEF would appear to heavily trade sensitivity for specificity.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.041
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
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 head0.070
GPT teacher head0.301
Teacher spread0.231 · 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

Citations32
Published2008
Admission routes1
Has abstractyes

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