Assessment of blood pressure skills and belief in clinical readings
Bibliographic record
Abstract
Accurate blood pressure (BP) measurement is essential for the diagnosis and management of hypertension. In clinical practice, BP is estimated using noninvasive methods with significant variability of application of guidelines in clinical practice, impacting the accuracy and certainty of BP measurements. We sought to assess how BP is measured in clinical practice. A survey was administered through professional societies that included predominantly cardiologists. Assessment of adherence to guideline recommendations for BP assessment was measured and compared to the level of confidence in clinic BP measurement. There were 571 surveys completed. The majority of respondents were cardiologists (61.1%), with 47 preventive cardiologists. BP was routinely checked in both arms by 53% at the initial visit, 48% check BP once each visit, and 64% wait 5 min before initial BP assessment. Automated BP assessment is used by 58% respondents. The majority (83%) trust their BP readings, and those who trust their BP readings are more likely to perform the initial BP assessment themselves, compared to those who do not trust the clinic BP readings (30.2% vs. 13.6%, P = 0.009). Accurate BP measurement is performed by 23% of cardiologists, and more likely performed accurately by a preventive cardiologist (38.3%) compared with other cardiologists (20.0%, P = 0.007). Accurate BP measurement is more likely for those who perform the initial BP themselves rather than any other staff (36.8% vs. 17.9%; P<0.001); and for those who repeat BP manually (80% vs. 54%; P<0.001), compared to those who do not measure BP accurately. Despite the inaccuracy of BP measurement, there is a high level of confidence in the BP readings. Accurate BP assessment continues to remain suboptimal in clinical practice. Reliability of BP assessment requires education, identifying barriers to implementation of recommendations and engagement of the entire team to improve BP assessment.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".