Bibliographic record
Abstract
Those unfamiliar with the health field and, unfortunately, many health professionals consider blood pressure (BP) measurement a triviality.It is understandable that those "delegated" health procedures and techniques, which contribute to saving patients' lives, lead to greater professional gratification than the measuring of BP.However, the results obtained trigger a multitude of acts and prescriptions that are not without consequence.Buus-Frank (2003) advises nursing personnel neither to underestimate themselves because they don't believe they perform vital health care functions, nor to limit themselves because they have too few letters after their names.BP monitoring is a complex, everyday technique that shouldn't be "swept under the rug" because it is considered a routine procedure (Costan, 2003).Imagine a situation in which your electronic sphygmomanometer displays a result of 180/124.Surprised by this result, you retake the BP, this time with a manual device, and obtain a very different result.Which device is the most reliable?The answer to this question involves many scientific, physiological and technical dimensions as described in the following pages.This article reviews the basic principles of an efficient BP measurement -a review that may surprise some readers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".