Bibliographic record
Abstract
Hypertension is a described as a “silent killer.” The condition is characterized by a clinically significant increase in the force that the blood exerts on the blood vessel walls and is diagnosed when a patient has a blood pressure of above 130/80 mmHg compared to a normal blood pressure of around 120/80 mmHg (Jiang et al., 2016). Hypertension is oftentimes not associated with any symptoms, allowing it to impact the health of millions of people across the world without them knowing (Jiang et al., 2016). Although an individual’s blood pressure typically rises as they grow older, hypertension can affect people of all ages and does not only impact older adults. There are many methods for measuring blood pressure and each approach has its own benefits and weaknesses. Personally, I was diagnosed with primary hypertension at a young age. Primary hypertension, sometimes referred to as essential hypertension, is defined as abnormally high blood pressure with no known cause (Lockett, 2021). As such, at an older age, my avatar will continue to manage this same health issue that I currently face. I have used many different blood pressure monitoring devices and have had issues with all of them. This lack of a suitable technology that can constantly monitor my blood pressure in an accurate, inconspicuous, and inexpensive way inspired me to investigate the potential benefits of a wearable blood pressure watch which also administers antihypertensive medication, like an insulin pump. Hypertension management watches have the potential to improve the arduous process of blood pressure control for patients while resolving current disparities in human healthcare.
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".