Top studies of 2021 relevant to primary care
Bibliographic record
Abstract
Objective To identify and summarize the most impactful medical articles published in 2025 relevant to primary care. Selecting the evidence The Patients, Experience, Evidence, Research (PEER) team, a Canadian evidence-based medicine research group with a focus on primary care, identified randomized controlled trials (RCTs) and meta-analyses relevant to primary care by reviewing the table of contents of major medical journals and medical email alert services. The articles were evaluated, ranked by the PEER team, and summarized. Main message The most impactful articles addressed a variety of clinical areas in primary care. Topics included the following: bedtime administration of blood pressure medications; comparison of tirzepatide and semaglutide for weight loss; β-blockers in patients with preserved ejection fraction heart failure; treatment of male partners of women diagnosed with bacterial vaginosis; early exposure of surgical wounds to water; impact of noise or arm position on blood pressure readings; mirtazapine for chronic insomnia; evolocumab for primary prevention of cardiovascular events; and emerging evidence for glucagonlike peptide-1 (GLP-1) agonist therapy. An honourable mention was made for several RCTs published on dual antiplatelet therapy. Conclusion Several clinical trials and meta-analyses published in 2025 were relevant to primary care, particularly in the areas of cardiology, infectious diseases, and GLP-1 agonist therapy.
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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.010 |
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".