6.1 UNSUPERVISED NON-INVASIVE MEASUREMENT OF AORTIC PULSE TRANSIT TIME BY MEANS OF ELECTRICAL IMPEDANCE TOMOGRAPHY
Bibliographic record
Abstract
Objectives: To examine the relationship between haemostatic factors, pulse wave velocity (PWV), blood pressure (BP) and mortality in British Europeans, African-Caribbeans (AfC) and Gujarati Indians.Design and Methods: Prospective cohort study of 331 subjects (40-79 years), followed-up over 21 years for mortality.PWV, Apolipoprotein-A1 (Apo-A1), apolipoprotein-B (Apo-B), factor VIIc (FVIIc), fibrinogen and vWF were measured at baseline in 118 Europeans, 100 AfC and 113 Gujaratis.Results: 113 (34%) subjects died during a mean of 16.8 years follow-up with 57 cardiovascular deaths.Women had significantly higher, and AfC males the lowest FVIIc and Apo-A1 levels.HDL levels were lowest (F Z 3.13; PZ0.04) in Gujarati Indians.Baseline age-sex and ethnicity adjusted FVIIc levels were higher in those who died (133.9 vs. 117.6%;PZ0.03), with similar levels of the other haemostatic factors by mortality status.In similarly adjusted partial correlations, Apo-A1 was inversely related to PWV (r Z -0.23, PZ0.04).No independent associations were found between fibrinogen, FVIIc, Apo-B, ApoB/Apo-A1 ratio, vWF and PWV.In Kaplan-Meier curves (Figure 1), those above, compared with those below the median of Apo-A1 levels, had reduced mortality.In Cox regressions, SBP (per 5mmHg) was associated with a 9%, PWV a 20% (per m/s), and FVIIc a 6% (per 10-unit; HR 1.06 (1.01, 1.10, PZ0.016) increased risk of mortality.Conclusions: The relationship between haemostatic variables with cardiovascular disease is well known, however few studies report their association with arterial stiffness.The results here are consistent with the independent effect of haemostatic variables influencing arterial stiffness and mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".