Diagnostic precision of mentally estimated home blood pressure means
Bibliographic record
Abstract
CONTEXT: Paper home blood pressure (HBP) charts are commonly brought to physicians at office visits. The precision and accuracy of mental calculations of blood pressure (BP) means are not known. METHODS: A total of 109 hypertensive patients were instructed to measure and record their HBP for 1 week and to bring their paper charts to their office visit. Study section 1: HBP means were calculated electronically and compared to corresponding in-office BP estimates made by physicians. Study section 2: 100 randomly ordered HBP charts were re-examined repetitively by 11 evaluators. Each evaluator estimated BP means four times in 5, 15, 30, and 60 s (random order) allocated for the task. BP means and diagnostic performance (determination of therapeutic systolic and diastolic BP goals attained or not) were compared between physician estimates and electronically calculated results. RESULTS: Overall, electronically and mentally calculated BP means were not different. Individual analysis showed that 83% of in-office physician estimates were within a 5-mmHg systolic BP range. There was diagnostic disagreement in 15% of cases. Performance improved consistently when the time allocated for BP estimation was increased from 5 to 15 s and from 15 to 30 s, but not when it exceeded 30 s. CONCLUSION: Mentally calculating HBP means from paper charts can cause a number of diagnostic errors. Chart evaluation exceeding 30 s does not significantly improve accuracy. BP-measuring devices with modern analytical capacities could be useful to physicians.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".