Variation in left ventricular cardiac magnetic resonance normal reference ranges: systematic review and meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: To determine population-related and technical sources of variation in cardiac magnetic resonance (CMR) reference ranges for left ventricular (LV) quantification through a formal systematic review and meta-analysis. METHODS AND RESULTS: This study is registered with the International Prospective Register of Systematic Reviews (CRD42019147161). Relevant studies were identified through electronic searches and assessed by two independent reviewers based on predefined criteria. Fifteen studies comprising 2132 women and 1890 men aged 20-91 years are included in the analysis. Pooled LV reference ranges calculated using random effects meta-analysis with inverse variance weighting revealed significant differences by age, sex, and ethnicity. Men had larger LV volumes and higher LV mass than women [LV end-diastolic volume (mean difference = 6.1 mL/m2, P-value = 0.014), LV end-systolic volume (MD = 4 mL/m2, P-value = 0.033), LV mass (mean difference = 12 g/m2, P-value = 7.8 × 10-9)]. Younger individuals had larger LV end-diastolic volumes than older ages (20-40 years vs. ≥65 years: women MD = 14.0 mL/m2, men MD = 14.7 mL/m2). East Asians (Chinese, Korean, Singaporean-Chinese, n = 514) had lower LV mass than Caucasians (women: MD = 6.4 g/m2, P-value = 0.016; men: MD = 9.8 g/m2, P-value = 6.7 × 10-5). Between-study heterogeneity was high for all LV parameters despite stratification by population-related factors. Sensitivity analyses identified differences in contouring methodology, magnet strength, and post-processing software as potential sources of heterogeneity. CONCLUSION: There is significant variation between CMR normal reference ranges due to multiple population-related and technical factors. Whilst there is need for population-stratified reference ranges, limited sample sizes and technical heterogeneity precludes derivation of meaningful unified ranges from existing reports. Wider representation of different populations and standardization of image analysis is urgently needed to establish such reference distributions.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it