Seasonal variation in blood pressure recorded in routine primary care
Bibliographic record
Abstract
Abstract Objective Seasonal variations in blood pressure (BP) exist. There is limited information about important clinical factors associated with increased BP and the strength and amplitude of seasonal variation in primary care. Methods This was a repeated cross-sectional observational study of routinely measured BPs in primary care using data from electronic medical records in the greater Toronto region, from January 2009 to June 2019. We used time-series models and mean monthly systolic BPs (SBPs) and diastolic BPs (DBPs) to estimate the strength and amplitude of seasonal oscillations, as well as their associations with patient characteristics. Results 314,518 patients were included. Mean SBPs and DBPs were higher in winter than summer. There was strong or perfect seasonality for all characteristics studied, except for BMI less than 18.5 (underweight). Overall, the mean maximal amplitude of the oscillation was 1.51mmHg for SPB (95% CI 1.30mmHg to 1.72mmHg) and 0.59mmHg for DBP (95% CI 0.44mmHg to 0.74mmHg). Patients aged 81 years or older had larger SBP oscillations than younger patients aged 18 to 30 years; the difference was 1.20mmHg (95% CI 1.15mmHg to 1.66mmHg). Hypertension was also associated with greater oscillations, difference 0.53mmHg (95% CI 0.18mmHg to 0.88mmHg). There were no significant differences in SBP oscillations by other patient characteristics, and none for DBP. Conclusion Strong seasonality was detected for almost all patient subgroups studied and was greatest for older patients and for those with hypertension. The variation in BP between summer and winter should be considered by clinicians when making BP treatment decisions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".