SS33-03 SHIFT WORK AND CARDIOVASCULAR DISEASES: CURRENT EVIDENCES AND GUIDELINES FOR PREVENTION
Bibliographic record
Abstract
Abstract Introduction Scientific evidence and recent large cohort studies indicate that many questions remain unanswered about relations between shift work (SW) and cardiovascular disease (CVD). Materials and Methods Large cohort studies and new clinical experimental research exploring the association of cardiovascular autonomic control, inflammation and sleep impairment with SW are revised. Wearable devices (WDs) and artificial intelligence (AI) techniques recently proposed to assess CVD biomarkers during working activity, sleeping and leisure time have also been explored. Results SW exposure intensity and new insights on age-dependent SW threshold require rigorous application in the workplace. Individual cardiovascular risk profile, including autonomic functioning, psychosocial and socio-economic conditions and incident CVD including hypertension, should be addressed to identify and manage hypersusceptible workers and to prevent post-retirement disability. It has been recently observed that shift work increases myocardial infarction reperfusion injury (EARLY-MYO-CMR registry). Gender differences in SW exposure, work-life balance and family-load remain poorly addressed. WDs and AI provide opportunities to identify circadian disruption, early signs of fatigue, low quality of sleep and sleep deprivation in SW. Finally, it is time to propose workplace intervention (SW duration and rotation, rest periods, facilities, and education) that may be individually tailored. Conclusions Though relations between SW and CVD including coronary heart disease have long been studied, the aforementioned gaps remain. Advanced technologies may furnish novel insights for workplace intervention aimed at reducing the effect of SW in CVD.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".