3T MRI investigation of cardiac left ventricular structure and function in a UK population: The tayside screening for the prevention of cardiac events (TASCFORCE) study
Bibliographic record
Abstract
Purpose To scan a volunteer population using 3.0T magnetic resonance imaging (MRI). MRI of the left ventricular (LV) structure and function in healthy volunteers has been reported extensively at 1.5T. Materials and Methods A population of 1528 volunteers was scanned. A standardized approach was taken to acquire steady‐state free precession (SSFP) LV data in the short‐axis plane, and images were quantified using commercial software. Six observers undertook the segmentation analysis. Results Mean values (±standard deviation, SD) were: ejection fraction (EF) = 69 ± 6%, end diastolic volume index (EDVI) = 71 ± 13 ml/m2, end systolic volume index (ESVI) = 22 ± 7 ml/m2, stroke volume index (SVI) = 49 ± 8 ml/m2, and LV mass index (LVMI) = 55 ± 12 g/m2. The mean EF was slightly larger for females (69%) than for males (68%), but all other variables were smaller for females (EDVI 68v77 ml/m2, ESVI 21v25 ml/m2, SVI 46v52 ml/m2, LVMI 49v64 g/m2, all P < 0.05). The mean LV volume data mostly decreased with each age decade (EDVI males: –2.9 ± 1.3 ml/m2, females: –3.1 ± 0.8 ml/m2; ESVI males: –1.3 ± 0.7 ml/m2, females: –1.7 ± 0.5 ml/m2; SVI males: –1.7 ± 0.9 ml/m2, females: –1.4 ± 0.6 ml/m2; LVMI males: –1.6 ± 1.1 g/m2, females: –0.2 ± 0.6 g/m2) but the mean EF was virtually stable in males (0.6 ± 0.6%) and rose slightly in females (1.2 ± 0.5%) with age. Conclusion LV reference ranges are provided in this population‐based MR study at 3.0T. The variables are similar to those described at 1.5T, including variations with age and gender. These data may help to support future population‐based MR research studies that involve the use of 3.0T MRI scanners. J. Magn. Reson. Imaging 2016;44:1186–1196.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".