Evaluation of cardiovascular disease risk factors in healthcare workers
Bibliographic record
Abstract
D -data Interpretation, E -Manuscript preparation, F -literature search, G -Funds collection Background. cardiovascular diseases (cvd) are chronic diseases that can be asymptomatic for a long time, and the first symptom may be sudden death. Objectives. This study was designed to draw attention to the frequency of both individual and occupational cardiovascular risk factors and to warn health professionals about variable risk factors. Material and methods. This research was conducted between 01.03.2022-01.09.2022. 160 participants were included in the study. The questionnaire form in which sociodemographic data was asked, the international physical activity questionnaire (short) form and the work stress scale form were directed to the participants. Blood pressure, height, weight and waist circumference were measured, and cvd risks were calculated using the scoRE (systematic coronary Risk Evaluation) 2 cardiovascular risk estimation algorithm. Results. Medium, high and very high cvd risks were determined in 41.8% of the employees. The risk was found to be significantly different among occupational groups (p < 0.001) and economic status (p = 0.036). considering the relationship between shift work status and cvd risk, the risk was found to be significantly higher in those working only during day shifts compared to those working during alternating day and night shifts (p = 0.033). It has been shown that work stress does not increase the cvd risk of healthcare workers (HcW) (p = 0.857). However, it was observed that work stress significantly increases ldl and total cholesterol (p = 0.026 and p = 0.018). Conclusions. In this study, it is emphasised that work-related risks should be taken into consideration, as well as individual cvd risks.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".