Use of Directly Observed Therapy to Assess Treatment Adherence in Patients With Apparent Treatment-Resistant Hypertension
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
to nurse practitioners and physician assistants, prescribing decisions may involve pharmacists; registered nurses who may supervise treatment of chronic disease, implement standing orders, and educate patients about new therapies; and procedural nurses who may arrange visits from medical device representatives and oversee purchasing. In Australia, Karanges et al found that many nonphysicians received speaking fees, highlighting their influential role. 3 The expansion of the Sunshine Act in the United States to include additional prescribing clinicians is a welcome development, although still several years away. The Australian experience with broader reporting of industry payments to health care professionals suggests that the forthcoming data should be illuminating. The next steps are to move from greater transparency to reforms that address the high costs of medical care and diminish the incentives for industry payments to health care professionals in the first place.
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.
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) | 0.001 | 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 it