Additional file 1 of 3D whole-heart grey-blood late gadolinium enhancement cardiovascular magnetic resonance imaging
Bibliographic record
Abstract
Additional file 1: Figure S1. Analysis performed by observer 2. a Expert image analysis for scar detection with the 17 segment AHA model. b Scar transmuarlity score performed on the patients showing a myocardial scar. Patient 5 was excluded from the analysis because contrast retainment was due to non-ischemic cardiomyopathy. c Comparison between 2 and 3D measurement of scar mass performed via Bland Altman analysis. Figure S2. Expert image quality assessment (1: non-diagnostic, 2: poor, 3: good and 4: excellent diagnostic quality) for all the acquired patients. Comparable results were obtained with the 3D and 2D approaches by both observers. An inferior quality score was obtained with the proposed method only for Patient 3 due to water/fat swaps in subcutaneous fat regions, which however did not affect scar detection. Figure S3. Inter-observer variability of scar mass quantification for 2D and 3D LGE PSIR acquisitions. Inter-observer variability was quantified via Bland Altman analysis of scar mass measurements performed by the two observers for both the 2D and 3D grey-blood LGE PSIR acquisitions. Figure S4. Effect of motion correction of IR and reference datasets on the PSIR reconstructed images. Top row: PSIR image calculated from motion corrected IR and reference volumes. Bottom row: PSIR image calculated from motion corrected IR volume and no motion corrected reference volume. Sharp scar delineation is obtained in the PSIR image obtained with motion correction performed on both IR-prepared and reference volume (blue line). Impaired scar delineation is obtained in the PSIR image reconstructed performing the motion correction only on the IR-prepared dataset as shown from the signal intensity profile across the left ventricle (orange line).
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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.840 | 0.185 |
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".