Enhancing central blood pressure accuracy through statistical modeling: A proof-of-concept study
Bibliographic record
Abstract
Background: Non-invasive estimation of central blood pressure (BP) may have better prognostic value than brachial BP. The accuracy of central BP is limited in certain populations, such as in females and the elderly. This study aims to examine whether statistical modeling of central BP for clinical and hemodynamic parameters results in enhanced accuracy. Methods: This study is a cross-sectional analysis of 500 patients who underwent cardiac catheterization. Non-invasive brachial cuff and central BP were measured simultaneously to invasive aortic systolic BP (AoSBP). Central BP was calibrated for brachial systolic (SBP) and diastolic BP (Type I calibration; C1SBP) or brachial mean and diastolic BP (Type II calibration; C2SBP). Differences between central SBP and the corresponding AoSBP were assessed with linear regression models using clinical and hemodynamic parameters. These parameters were then added to C1SBP and C2SBP in adjusted models to predict AoSBP. Accuracy and precision were computed in the overall population and per age or sex strata. Results: C1SBP underestimated AoSBP by 11.2 mmHg (±13.5) and C2SBP overestimated it by 6.2 mmHg (±14.8). Estimated SBP amplification and heart rate were the greatest predictors of C1- and C2-AoSBP accuracies, respectively. Statistical modeling improved both accuracy (0.0 mmHg) and precision (±11.4) but more importantly, eliminated the differences of accuracy seen in different sex and age groups. Conclusion: Statistical modeling greatly enhances the accuracy of central BP measurements and abolishes sex- and age-based differences. Such factors could easily be implemented in central BP devices to improve their accuracy.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".