Lifestyle Influences Changes in Fibrin Clot Properties Over a 10-Year Period on a Population Level
Bibliographic record
Abstract
Case-control and observational studies have provided a plausible mechanistic link between clot structure and thrombosis. We aimed to identify lifestyle, demographic, biochemical, and genetic factors that influence changes in total fibrinogen concentration and clot properties over a 10-year period in 2,010 black South Africans. Clot properties were assessed with turbidimetry and included lag time, slope, maximum absorbance, and clot lysis time. Linear mixed models with restricted maximum likelihood were used to determine whether (1) outcome variables changed over the 10-year period; (2) demographic and lifestyle variables, biochemical variables, and fibrinogen single-nucleotide polymorphisms influenced the change in outcome variables over the 10-year period; and (3) there was an interaction between the exposures and time in predicting the outcomes. A procoagulant risk score was furthermore created, and multinomial logistic regression was used to determine the exposures that were associated with the different risk score categories. In this population setting, female gender, obesity, poor glycemic control, increased low-density lipoprotein cholesterol, and decreased high-density lipoprotein cholesterol contributed to the enhanced progression to prothrombotic clot properties with increasing age. Alcohol consumption on the other hand, offered a protective effect. The above evidence suggest that the appropriate lifestyle changes can improve fibrin clot properties on a population level, decreasing cardiovascular disease risk and thus alleviate the strain on the medical health care system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".