An assessment of intra-individual variability in carotid artery longitudinal wall motion: recommendations for data acquisition
Bibliographic record
Abstract
OBJECTIVE: To assess the intra-individual variation in carotid artery longitudinal wall motion (CALM) in healthy adults in order to determine the amount of data required to generate a representative measurement of CALM. APPROACH: We conducted an analysis of 27 healthy men to determine whether calculation of resting individual CALM outcomes is dependent on the number of averaged heart cycles. CALM was assessed at rest, 1-2 cm proximal to the right carotid bifurcation during a breath hold and was segmented into three motion displacements: systolic anterograde CALM, systolic retrograde CALM, and diastolic CALM. A 2D measure of total carotid artery motion (RALength) was also determined from the longitudinal and radial displacements. Outcomes were averaged discretely using two, three, four, five, and six consecutive heart cycles to assess the impact of additional data on intra-individual coefficients of variation (CV%) and intra-class correlations (ICC). MAIN RESULTS: Calculated means were similar between all heart cycle averaging for CALM displacements (all P > 0.05), though the two-heart cycle average of RALength (P = 0.06) was reduced in comparison to all other averages. Averaging data from four heart cycles was sufficient to generate a plateau in the variability in resting CALM displacements, such that within-subject ICC values all reached >0.90 and CV% were similar to previously reported day-to-day variability in healthy adults. SIGNIFICANCE: There is variability in beat-to-beat measures of CALM that should be considered when designing protocols for data collection and analysis. We suggest that four consecutive heart cycles should be averaged to generate representative resting CALM outcomes in humans. While indices of variability were reduced when assessing outcomes generated from two to four heart cycles, no further improvements were observed when more heart cycles were included, indicating that averaging more than four heart cycles is likely not required.
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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 | 0.062 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".