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. This article has been peer reviewedLe magazine des infirmières et infirmiers d'urgence du Québec
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.014 |
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".