The Accuracy in Measurement of Blood Pressure (AIM‐BP) collaborative: Background and rationale
Bibliographic record
Abstract
Blood pressure (BP) measurement, a technique first described over \na century ago, is an essential component of clinical care and critical \nfor the detection and management of hypertension.1 \n Accordingly, \nthe ramifications of inaccurate BP measurement, which is a per‐ \nsistent and pervasive problem worldwide, are profound.2 \n Assuming \na global prevalence of hypertension of 1.4 billion,3 \n a 5‐mmHg error \nin BP measurement has been estimated to result in the incorrect \nclassification of hypertension status in at least 84 million individ‐ \nuals worldwide.4 \n In addition, incorrect classification has important \nramifications for individual patients, whether it leads to misdiagno‐ \nsis and inappropriate prescribing of antihypertensive drugs, or to \nlack of recognition of an clinical condition that can cause devastat‐ \ning cardiovascular consequences. The continued rise in the global \nprevalence of hypertension has made work related to optimizing BP \nmeasurement even more critical, and notwithstanding similar initia‐ \ntives that have been conducted or are ongoing, additional efforts \nto improve BP measurement on a global scale are clearly needed.5
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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 | 0.118 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".