Assessment of adherence to treatment in patients with resistant arterial hypertension according to Russian-language literature. A systematic review
Bibliographic record
Abstract
Objective. To study the possibility of assessing the adherence to therapy in patients with resistant hypertension (RH) according to Russian-language literature. Material and methods. RH was defined according to clinical guidelines of the Russian Medical Society on Arterial Hypertension (2021). Data were searched in electronic databases and searching engines Google Scholar, eLIBRARY.RU, Central Scientific Medical Library (database inception to 01.12.2023). According to inclusion and exclusion criteria, two independent researchers extracted the articles. Disagreements between researchers were resolved by the third researcher. Study quality was assessed using the Newcastle-Ottawa scale. Results. Among 1610 primary identified publications, 1473 studies were screened. Finally, the authors assessed 49 full-text articles for eligibility. Four studies were selected for qualitative analysis. There were 256 patients. Adherence was assessed using the Morisky—Green questionnaire in original or modified versions. Analysis revealed significant heterogeneity in results. Discussion. Objective assessment of adherence to treatment in this disease and development of measures for its improvement are associated with certain difficulties that limits the number of available studies. The authors performed a systematic review and assessed the possibility of structuring and analyzing the information presented in modern Russian-language literature. Conclusion. Most results indicate low adherence among patients that suggests lower real prevalence of RH, as well as reduced effectiveness of treatment due to non-compliance with drug regimen.
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 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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".