Top studies of 2023 relevant to primary care
Bibliographic record
Abstract
OBJECTIVE: To provide a summary of the noteworthy medical articles published in 2023 that are relevant to family physicians. SELECTING THE EVIDENCE: Articles were chosen and ranked by the PEER (Patients, Experience, Evidence, Research) team, a group of primary care health professionals focused on evidence-based medicine. The selection process involved routine surveillance of tables of contents in high-impact medical journals and continuous monitoring of EvidenceAlerts. Articles were prioritized based on their direct applicability to and potential to influence primary care practice. MAIN MESSAGE: Selected articles addressed various clinical areas of primary care. The topics included a comparison of a treat-to-target approach versus a high-intensity statins prescription for lipid management; semaglutide and its impact on cardiovascular outcomes; respiratory syncytial virus vaccine for older adults; chlorthalidone versus hydrochlorothiazide in preventing cardiovascular events; amitriptyline for irritable bowel syndrome; the role of opioids in acute back pain; safety of oral penicillin challenges in patients allergic to penicillin; spironolactone for facial acne; strategies to reverse frailty in older adults; and identifying the provider of chronic disease management. Two "up and coming" medications are also mentioned: retatrutide for weight loss and fezolinetant for vasomotor symptoms of menopause. CONCLUSION: Research published in 2023 yielded several high-quality articles with topics relevant to primary care, including cardiovascular care, irritable bowel syndrome, care of the elderly, and acne management.
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 | 0.016 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.045 | 0.042 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".