Investigating the European Society of Cardiology Diastology Guidelines in a practical scenario
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".