Abstract 13903: Accurate Assessment of Hypertension and Belief in Clinical Readings by Cardiologists
Bibliographic record
Abstract
Background: The accurate measurement of blood pressure (BP) is essential for the diagnosis & management of hypertension. In clinical practice, BP is estimated using noninvasive methods but there is significant variability in practice, despite guidelines and recommendations for accurate BP assessment. Individual clinical practices implementation of guidelines for BP assessment influence accuracy and clinical certainty of BP measurements. Hypothesis: We sought to assess how BP is assessed in clinical practice of cardiologists and to assess the clinical certainty of the BP obtained in their clinics. Methods: A survey was administered through professional societies that include predominately cardiologists & via Twitter. Assessment of adherence to guideline recommendations for BP assessment was measured and compared to belief and reliability of BP assessment in clinic. Results: 612 surveys were completed in 30 days, 364 completed by cardiologists; 49 (13%) preventive cardiologists. Majority of cardiologists based in United States. 53% routinely check BP in both arms at initial visit, 48% check BP only 1X/per visit; 64% wait 5 minutes before initial BP assessment. Automated BP assessment is used in 58% of respondents’ clinics. 83% trust their BP readings in clinic. Only 23% (85) of all cardiologist accurately measure BP as recommended by guidelines & it is more likely to be done by a preventive cardiologist (P=0.017). For those who do measure BP correctly, 80% repeat BP manually compared with 54% who do not measure BP accurately (P<0.001). For those who perform BP accurately, 86% report trusting their BP readings in clinic, similar to those who assess BP inaccurately (P=0.45). Conclusions: Accurate BP assessment by cardiologists remains suboptimal. Reliability of BP assessment in clinic requires education, implementation of recommendations and empowerment of the entire team to improve BP assessment & results in improved cardiovascular outcomes for our patients.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".