Impact of Terminal Digit Preference by Family Physicians and Sphygmomanometer Calibration Errors on Blood Pressure Value: Implication for Hypertension Screening
Bibliographic record
Abstract
The accuracy of blood pressure (BP) measurement is important; systematic small errors can mislabel BP status in many persons. The objective of this study was to assess the impact of 2 types of measurement errors on the evaluation of BP in family medicine: errors associated with terminal digit preference and those associated with calibration errors of sphygmomanometers. Secondary data analyses from 2 different projects were used to derive empiric distributions of terminal digit and BP device errors. Taking into account both types of errors, the proportion of false positives (falsely high BP) and false negatives (falsely normal BP) varied between 0. 82% and 5.18% of the population of consulting family physicians. In the United States, false positives and false negatives in patients' BP evaluations might lead to overtreating or undertreating 1.15 million to 7.25 million patients. Results support the need for the development of systematic interventions for quality control of BP measurements and periodic retraining for health professionals.
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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 | 0.059 | 0.374 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".