Evidence that Shift Work Influences the Relationship Between Reactivity to Mental Stress and Indices of Vascular Health: A Pilot Study
Bibliographic record
Abstract
High cardiovascular reactivity to stressful tasks is a predictor of increased future cardiovascular risk and may play a mechanistic role in disease progression. The purpose of this investigation was to examine the relationship between acute cardiovascular stress reactivity and indicators of vascular health status in shift workers (SW) and non‐shift workers (NSW). Female hospital employees were recruited. 18 NSW and 19 SW (minimum 6 yrs experience) participated. Blood pressure (Finometer), central pulse wave velocity (PWV) (carotid to femoral; applanation tonometry) and common carotid artery intima media thickness (IMT) (echo ultrasound) were assessed at rest. With continued blood pressure assessment, participants then completed a 20 min mental stress task with speech and arithmetic components (based on the Trier Social Stress Test). Systolic blood pressure (SBP) reactivity was quantified as the difference between the resting baseline SBP and the SBP during the stress task. Data are mean ± SD. SW and NSW did not differ with respect to age (42 ± 11 yrs, p=0.563), central PWV (6.6 ± 1.3 m/s, p=0.416), IMT (0.47 ± 0.11 cm, p=0.207) or SBP reactivity (20.3 ± 11.2 mmHg, p=0.717). In NSW there was no relationship between SBP reactivity and either PWV (p=0.789; r=0.07) or IMT (p=0.876; r=0.06). In contrast in SW, both PWV (p=0.032; r=0.507) and IMT (p=0.062; r=0.435) increased with increasing SBP reactivity. These data suggest that shift work experience may alter the relationship between cardiovascular stress reactivity and vascular health. Garfield Kelly Cardiovascular Research and Development fund, KGH.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".