Getting started with search filters in primary care literature reviews
Bibliographic record
Abstract
Primary care researchers and clinicians are facing an ever-growing evidence base, more options to access research evidence, and increasingly limited time. Incorporating search filters into primary care systematic reviews can significantly improve the efficiency and confidence of the search process. Search filters, or hedges, are predeveloped search strategies that combine controlled vocabulary and free text terms using Boolean operators (words like "AND," "OR"). Search filters help to manage the diverse terminology in the literature, such as the various synonyms for primary care, and can be tailored to the specific needs of the review, whether it aims to be exhaustive or more focussed. Resources such as specialized librarians, databases such as PubMed, and repositories such as the InterTASC Information Specialists Sub-Group provide access to these valuable tools. However, as primary care terminology continues to evolve, regular updates to these filters are necessary to maintain their relevance and effectiveness. This method brief presents search filters and highlights their value for finding research literature in primary care.
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 | 0.538 | 0.765 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.054 | 0.037 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".