Top studies relevant to primary care practice.
Bibliographic record
Abstract
OBJECTIVE: To summarize 10 high-quality studies from 2017 that have strong relevance to primary care practice. QUALITY OF EVIDENCE: Study selection involved routine literature surveillance by a group of primary care health professionals. This included screening abstracts of important journals and Evidence Alerts, as well as searching the American College of Physicians Journal Club. MAIN MESSAGE: Topics of the 2017 articles include whether treating subclinical hypothyroidism improves outcomes or symptoms; whether evolocumab reduces cardiovascular disease as well as low-density lipoprotein levels; whether lifestyle interventions reduce medication use in patients with diabetes; whether vitamin D prevents cardiovascular disease, cancer, or upper respiratory tract infections; whether canagliflozin reduces clinical events in patients with diabetes; how corticosteroid injections affect knee osteoarthritis; whether drained abscesses benefit from antibiotic treatment; whether patients with diabetes benefit from bariatric surgery; whether exenatide reduces clinical events in patients with diabetes; and whether tympanostomy tubes affect outcomes in recurrent acute otitis media or chronic otitis media. We provide brief summaries, context where needed, and final recommendations for 10 studies with potential effects on primary care. We also briefly review 5 "runner-up" studies. CONCLUSION: Research from 2017 produced several high-quality studies in diabetes management. These have demonstrated benefit for alternative therapies and offered evidence not previously available. This year's selection of studies also provided information on a variety of conditions and therapies that are, or might become, more common in primary care settings.
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 | 0.020 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".