Consolidated normal reference limits for right ventricular quantification by 3-dimensional echocardiography using a novel meta-analytic approach
Bibliographic record
Abstract
Purpose: Three-dimensional echocardiography (3DE) has superior accuracy and reproducibility for quantification of right ventricular (RV) size and function. However, the lack of consolidated normative data has been a factor in limiting its widespread clinical use. Methods: We performed a comprehensive review of the literature to identify all published studies that measured RV parameters by 3DE in healthy control groups. The parameters of interest were ejection fraction (RVEF), end-diastolic volume (RVEDV), end-systolic volume (RVESV); both non-indexed and indexed to body surface area. Age- and sex-specific normative data were extracted when available. Using a novel statistical approach, we performed a random-effects meta-analysis of these normative data to generate pooled mean values, upper reference values (mean + 2SD), lower reference values (mean – 2D), and 95% confidence intervals. Results: We identified 29 studies encompassing a total of 1610 healthy individuals. Semi-automated border detection algorithms were used in most studies. The results of the normative data meta-analysis are shown in the table. Using these results, the following 3DE cutoffs are recommended to define abnormality: RVEF <45.6%, RVEDV >124.0 mL, RVEDVi >84.3 mL/m2, RVESV >61.0 mL, and RVESVi >38.5 mL/m2. Few studies reported age-specific (N=2) and sex-specific (N=4) data. Each decade of advancing age was associated with a gradual mild decline in volumes and preserved RVEF. Female sex was associated with mildly higher RVEF and moderately lower indexed and non-indexed volumes compared to males. Meta-analysis of normal reference values Conclusions: The proposed reference limits consolidate the body of normative data for RV quantification using 3DE, and thus should facilitate the clinical adoption of this technique. Further data are needed to evaluate age- and sex-specific differences.
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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 | 0.076 | 0.170 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".