P954Seasonal changes in blood pressure using 24hr ambulatory BP monitoring. A large single centre study
Bibliographic record
Abstract
Background: Many clinical studies have reported a higher BP in the winter when compared to summer. Some studies have used 24hr ABPM but have enrolled relatively small numbers. We report on >20,000 ABPMs which adds further to the overall effect of this phenomenon. The daily temperature in our city in Canada averages -7.1 Celsius in January to +21.4 Celsius in July. Methods: Our database contains 28,923 ABPMs. Only the first ABPM for each patient was used in this analysis. Once repeat studies had been removed there were 20,315 separate ABPM studies. Daytime was arbitrarily defined as 7am to 10pm and nighttime from 10pm to 7am. ABPM data was divided into Spring, Summer, Autumn and Winter. ANOVA was used to determine overall statistical significance with Tukey-Kramer inter-comparison testing performed to assess differences between any two groups. A P value of <0.05 was considered statistically significant. Results: There were 10,679 females and 9,636 males with an average age of 58.0±14.4 years. The overall results are seen in Table 1. Figure 1 shows the results for average 24hr SBP. ANOVA showed an overall significant difference in average 24hr BP. However, this difference was accounted for by the average daytime BPs between Summer and Winter, the average nighttime BPs showing no significant difference. Furthermore, there was no significant difference between any of the BPs measured in Spring or Autumn. The difference in Summer and Winter for average daytime systolic BP was 2.3mmHg and for diastolic BP 1.2mmHg.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".