Choosing the optimal method of blood pressure measurement for limited‐resource rural communities in the “Community Health Assessment Program—Philippines”
Bibliographic record
Abstract
The Community Health Assessment Program-Philippines (CHAP-P) is an international collaboration of investigators whose aim is to adapt a previously proven Canadian community-based cardiovascular awareness and prevention intervention to the Philippines and other low-middle-income countries. Choosing a method of blood pressure measurement for the research program presents a challenge. There is increasing consensus globally that blood pressure measurement with automated devices is preferred. Recommendations from low-middle-income countries, including the Philippines, are less supportive of automated blood pressure devices. The value placed on factors including device accuracy, durability, cost, energy source, and complexity differ with local context. Our goal was to support the progress of local policy concerning blood pressure measurement while testing a comprehensive approach to community-based screening for cardiovascular risk. The authors describe the challenges in making a choice of blood pressure device and the approach to determine optimal method of measurement for our research program.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".