Cardiometabolic risk factors in the Swedish Werlabs cohort based on self-initiated health screening: cohort profile
Bibliographic record
Abstract
PURPOSE: There is limited research on individuals undergoing self-initiated health examinations, and the Werlabs cohort will be a base for such research. PARTICIPANTS: All individuals aged 18 or older who had undertaken a self-initiated health examination at Werlabs AB with at least one recorded value of creatinine or cholesterol in Sweden (from 1 January 2015 through 31 December 2023) was included. Medical history and anthropometric measurements were self-reported through an online questionnaire. We describe cohort baseline characteristics, demographic variables and cardiometabolic risk factors. FINDINGS TO DATE: ), 57.8% for a low-density lipoprotein cholesterol level of >3.0 mmol/L and 4.1% for anaemia (haemoglobin <120 g/L and <130 g/L for women and men, respectively). Interestingly, 1.5% of the individuals had a glucose measurement of >7.0 mmol/L, without reporting a previous diagnosis of diabetes. In an analysis restricted to 621 individuals with recorded blood pressure data between the age of 40 and 70 years and without existing cardiovascular disease, diabetes or kidney disease, 35,4% were classified as high or very high cardiovascular risk according to the 2021 ESC guidelines on cardiovascular disease prevention and with lipid levels that made them eligible for lipid-lowering therapy. FUTURE PLANS: The Werlabs cohort comprises a rather healthy and young population that can provide opportunities for future studies on individuals undergoing self-initiated health examinations and has the potential to impact treatment of cardiometabolic risk factors.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".