HOW DOES THE ANTIHYPERTENSIVE THERAPY OF EVERYDAY PATIENTS FOLLOW THE DIFFERENT GUIDELINES?
Bibliographic record
Abstract
Objective: The different guidelines have got therapeutic recommendations for different conditions with hypertension. It sounds perfect. But what is the situation in the real life? How does the antihypertensive therapy of everyday patients follow the different guidelines? Design and method: Medications of alltogether 453 patients were analysed retrospectively from case reports. The selection of the patients was not based on specific criteria, but they arrived for their next visit at one of the Hypertension Outpatient Clinics of the Nephrology-Hypertension Center of the University of Szeged between 06/01/2021 and 31/12/2021. The authors used the guidelines valid in 2021 of the European Hypertension Society, the Hungarian Hypertension Society, the American Heart Association, the Hypertension Canada and the NICE as a base. Diabetes mellitus, angina pectoris, acute myocardial infarction, atrial fibrillation, congestive heart failure, chronic kidney disease without proteinuria, renal failure with proteinuria and stroke/TIA were the analysed conditions. Results: The rates of recommendation follow-up are summarized in the table. Conclusions: The antihypertensive medication of the patients proved to follow recommendations well. There were only non-significant differences between the following recommendations. In cardiovascular conditions, which can be considered more serious, it was completely the same.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".