WHICH PATIENTS RETURN TO ANTIHYPERTENSIVE DRUG THERAPY AFTER DISCONTINUATION?
Bibliographic record
Abstract
Objective: Using a self-administered questionnaire, the objectives of this study were: (1) to identify the predictors that discriminate between the patients who consider returning to antihypertensive therapy and those who indicate that they will not return to drug therapy; (2) to compare the predictors of returning to antihypertensive drug therapy in a North American population (United States, and Canada) with a European (France, Germany, and Italy) population. Design: Cross-sectional study. Data Collection: An existing dataset was obtained from Bristol Myers Squibb (BMS), New Jersey. BMS recruited patients with a diagnosis of hypertension from five different countries (USA, Canada, France, Germany, and Italy) (n=731). Trained interviewers administered the questionnaire in one-on-one interview sessions at a research facility, interviewer's home, or patient's home. Methodology: Required variables were extracted from the dataset using SAS (Statistical Analysis System). The patients who said that they were already taking their antihypertensive medication as directed were deleted from the study. Therefore the final sample of 439 patients was used for the analyses. Independent variables were divided into four groups and logistic regression analyses were carried out separately for each sets of variables. The significant variables from each set of variables were identified and combined to develop a final logistic regression model. Finally, the study sample was divided into North American population and European population. A final logistic regression model was developed separately for these two populations. Results: The number of physician visits for blood pressure problems, number of medication additions to the ones that patients were already taking for their blood pressure, patients' satisfaction towards the assistance they received from their health care provider in managing high blood pressure, and patients' satisfaction with the medications that were available to use to manage their blood pressure were identified as significant predictors in the final model. The European population showed two significant predictors that include number of physician visits for blood pressure problems and patients' satisfaction with the medications that were available to use in manage their blood pressure. However, North American population showed only one significant predictor that is number of medication additions to the ones that patients were already taking to manage their blood pressure. No interaction terms were found to be significant. The model worked best for the set of psychological variables. Conclusion: The treatment of hypertension remains a difficult task. A frequent reason is poor adherence to the drug regimen. The results indicate that an
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".