Validity of Revised Doppler Echocardiographic Algorithms and Composite Clinical and Angiographic Data in Diagnosis of Diastolic Dysfunction
Bibliographic record
Abstract
BACKGROUND: Commonly used echocardiographic indices for grading diastolic function predicated on mitral inflow Doppler analysis have a poor diagnostic concordance and discriminatory value. Even when combined with other indices, significant overlap prevents a single group assignment for many subjects. We tested the relative validity of echocardiographic and clinical algorithms for grading diastolic function in patients undergoing cardiac catheterization. METHOD: Patients (n = 115), had echocardiograms immediately prior to measuring left ventricular (LV) diastolic (pre-A, mean, end-diastolic) pressures. Diastolic function was classified into the traditional four stages, and into three stages using a new classification that obviates the pseudonormal class. Summative clinical and angiographic data were used in a standardized fashion to classify each patient according to the probability for abnormal diastolic function. Measured LV diastolic pressure in each patient was compared with expected diastolic pressures based on the clinical and echocardiographic classifications. RESULT: The group means of the diastolic pressures were identical in patients stratified by four-stage or three-stage echocardiographic classifications, indicating that both classifications schemes are interchangeable. When severe diastolic dysfunction is diagnosed by the three-stage classification, 88% and 12%, respectively, were clinically classified as high and intermediate probability, and the mean LV pre-A pressures was >12 mmHg (P < 0.005). Conversely, the mean LV pre-A pressure in the clinical low probability or echocardiographic normal groups was <11 mmHg. CONCLUSION: Use of a standardized clinical algorithm to define the probability of diastolic function identifies patients with elevated LV filing pressure to the same extent as echocardiographic methods.
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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 | 0.051 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".