Quantification of circumferential, longitudinal, and radial global fractional shortening using steady‐state free precession cines: A comparison with tissue‐tracking strain and application in fabry disease
Bibliographic record
Abstract
PURPOSE: Conventional calculation of myocardial strain requires tissue-tracking. A surrogate for strain called global fractional shortening (GFS) is proposed based on changes in dimensions of endocardial and epicardial surfaces without tissue-tracking. METHODS: Three-dimensional endocardial and epicardial left ventricular surfaces traced at end-diastole and end-systole using conventional steady-state free precession cine images were used to calculate GFScc (circumferential), GFSll (longitudinal), and GFSrr (radial) using fractional length changes in each direction over the heart surface. GFS values were validated using finite element models (FEM) and in vivo using tagging-derived strains (εcc ,εll ,εrr ) in patients with a wide range of ejection fraction (EF) and diagnosis (n=32). GFS was also measured in 31 patients with Fabry disease and matched healthy controls. RESULTS: GFS values were within 3% of average FEM-derived Lagrangian strains and had good agreement in vivo (GFScc =-14 ± 4%, εcc =-14 ± 4%, R(2) =0.85; GFSll =-12 ± 4%, εll =-12 ± 4%, R(2) =0.72; GFSrr =46 ± 21%). εrr could not be measured reliably from tagging. Compared with healthy controls with matched EF, patients with Fabry disease had significantly increased GFScc (Endo) (-28 ± 3% versus -25 ± 2%), decreased GFScc(Epi) (-10 ± 2% versus -11 ± 2%) and decreased GFSll for all components. CONCLUSION: GFS yields similar values to conventionally measured strains without requiring tissue-tracking. Compared with controls, patients with Fabry disease have significant differences in several GFS components.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".