Laboratory testing and antihypertensive medication adherence following initial treatment of incident, uncomplicated hypertension: A real‐world data analysis
Bibliographic record
Abstract
In this study on medication adherence among newly diagnosed patients with uncomplicated, incident hypertension, we conducted a retrospective cohort study using available administrative and laboratory data from April 1, 2012 to March 31, 2017 in Alberta, Canada to understand the extent to which baseline laboratory assessment and/or subsequent follow-up was associated with persistence with antihypertensive therapy. We determined the frequency of baseline and follow-up testing and compared the rates of medication persistence by patient-, neighbourhood-, and treatment-related factors. Of 103 232 patients with newly diagnosed, uncomplicated hypertension who filled their first prescription within our study timeframe, 52.5% were non-persistent within 6 months. Persistent patients were more often female and residing in neighbourhoods with higher social status (with exception to rurality). Aside from older age, the strongest predictor of persistence was performance of laboratory testing related to hypertension with an apparent effect in which higher levels of medication persistence were seen with more frequent laboratory testing. We concluded that medication persistence was far from optimal, dropping off considerably after 6 months for more than half of patients. Medication persistence is a substantial barrier to realizing the full societal benefits of antihypertensive treatment. Ongoing follow up with patients, including laboratory testing, may be a critical component of better long term treatment persistence.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".