Global longitudinal active strain energy density (GLASED): a powerful prognostic marker in a community-based cohort
Bibliographic record
Abstract
AIMS: Identifying the imaging method that best predicts all-cause mortality, cardiovascular adverse events, and heart failure risk is crucial for tailoring optimal management. Potential prognostic markers include left ventricular (LV) myocardial mass, ejection fraction, myocardial strain, stroke work, contraction fraction, pressure-strain product, and a new measurement called global longitudinal active strain density (GLASED). This study sought to compare the utility of 23 potential LV prognostic markers of structure and contractile function in a community-based cohort. METHODS AND RESULTS: The impact of cardiovascular magnetic resonance image-derived markers extracted by machine learning algorithms was compared with the future risk of adverse events in a group of 44 957 UK Biobank participants. Most markers, including the LV ejection fraction, have limited prognostic value. GLASED was significantly associated with all-cause mortality and major adverse cardiovascular events, with the largest hazard ratio, highest ranking, and differentiated risk in all three tertiles (P ≤ 0.0003). CONCLUSION: GLASED predicted all-cause mortality and major cardiovascular adverse events better than conventional markers of risk and is recommended for assessing patient prognosis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".