Trends in systematic recording errors of blood pressure and association with outcomes in Canadian and UK primary care data: a retrospective observational study
Bibliographic record
Abstract
IntroductionEnd digit preference (EDP) or systematic bias in the recording of blood pressure (BP) measurement is prevalent in primary care: up to 60% of BP readings end in zero. High blood pressure (BP) is a leading cause of increased morbidity in adults and errors in measurement may contribute to increased rate of adverse cardiovascular outcomes. Objectives and ApproachWe studied EDP trends, uptake of Automated Office BP (AOBP) measurement, and cardiovascular outcomes in the UK and Canada.This is a retrospective observational study using routinely collected Electronic Medical Record data for patients age 18 or more. We used bootstrap method to estimate the odds ratios where logistic regression was fitted on one thousand independently sampled replicates of the CPCSSN and RCGP datasets. We implemented the unsupervised algorithm of k-nearest neighbor across all sites to find the optimal decision boundary to classify the sites into the three categories: (1) strong EDP; (2) some EDP; (3) no EDP. ResultsThe mean rate of end digit zero for both systolic and diastolic BP decreased from 26.6% in 2006 to 15.4% in 2015 in Canada and from 24.2% in 2001 to 17.3% in 2015 in the U.K. There was a gradual decline in EDP in the three years following the purchase of an AOBP machine. Sites categorized as having high levels of EDP had lower mean sBP levels than sites with potentially no EDP in both Canada and UK. Patients in sites with high levels of EDP had higher yearly prevalence of stroke (Standardized morbidity ration or SMR 1.11), myocardial infarcts (SMR 1.15), and angina (SMR 1.27) than patients in sites with no EDP. Conclusion/ImplicationsThere is systematic recording errors including rounding down of BP readings associated with higher rates of EDP and presumably more use of manual BP measurement. Higher rates of EDP were associated with greater prevalence of adverse cardiovascular outcomes. Consideration should be given to using AOBP machines in primary care.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".