Top 2020 studies relevant to primary care
Bibliographic record
Abstract
OBJECTIVE: To summarize high-quality studies for 10 topics from 2020 that have strong relevance to primary care practice. SELECTING THE EVIDENCE: Study selection involved routine literature surveillance by a group of primary health care professionals. This included screening abstracts of high-impact journals and EvidenceAlerts, as well as searching the American College of Physicians Journal Club. MAIN MESSAGE: Topics of the 2020 articles most likely to affect primary care practice included whether antibiotic prophylaxis reduces maternal infections following operative vaginal birth; which second-line agent after metformin reduces cardiovascular outcomes for patients with diabetes; whether gabapentin is effective for alcohol use disorder; whether compression stockings prevent recurrent cellulitis; guideline recommendations for management of dyslipidemia to reduce cardiovascular risk; whether intermittent fasting is superior to consistent mealtimes for weight loss; whether vitamin C added to iron supplementation increases hemoglobin more than iron alone; whether antacid-lidocaine combinations are superior to antacid alone for epigastric pain; whether dapagliflozin improves renal and cardiovascular outcomes in chronic kidney disease; and whether empagliflozin improves cardiovascular outcomes in patients with heart failure. Five "runner-up" studies are also briefly reviewed. CONCLUSION: Research from 2020 produced several high-quality studies in diabetes and cardiovascular disease, but also included a variety of other conditions relevant to primary care such as vaginal operative births, alcohol use disorder, weight loss, and chronic leg edema.
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 | 0.027 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.031 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".