In-vivo MRI and in-vivo electro-anatomical voltage map characteristics of infarct heterogeneity in a swine model
Bibliographic record
Abstract
The arrhythmogenic substrate in patients with prior myocardial infarct (MI) is located at the border zone, BZ. In this study we correlated the BZ identified by two methods: electro-anatomical voltage mapping (EAVM) and a novel MRI method, multi-contrast late enhancement (MCLE). A pre-clinical porcine model with chronic MI was used to characterize BZ via MRI and EAVM. Results focus on the comparison between scar percentage and BZ percentage identified by each method. The correlation coefficient for BZ percentage between the two methods was 0.74 with a p-value of less the 0.0001. Bland-Altman plots were also used to compare between the two methods (slope of 0.83 ± 0.045). For a case of subtle infarct, there was only 1.3% infarct identified on EAVM compared to 22.2% on the corresponding slice on MCLE. The percentage of infarct on MCLE in subtle infarct does not relate to percentage of infarct in EAVM. Future registration between T(1) maps and EAVM will permit a quantitative comparison of MRI and EAVM measures.
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 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".