Improving the Reporting of Primary Care Research: Consensus Reporting Items for Studies in Primary Care—the CRISP Statement
Bibliographic record
Abstract
Primary care（PC）is a unique clinical specialty and research discipline with its own perspectives and methods. Research in this field uses varied research methods and study designs to investigate myriad topics. The diversity of PC presents challenges for reporting，and despite the proliferation of reporting guidelines，none focuses specifically on the needs of PC. The Consensus Reporting Items for Studies in Primary Care（CRISP）Checklist guides reporting of PC research to include the information needed by the diverse PC community，including practitioners，patients，and communities. CRISP complements current guidelines to enhance the reporting，dissemination，and application of PC research findings and results. Prior CRISP studies documented opportunities to improve research reporting in this field. Our surveys of the international，interdisciplinary，and interprofessional PC community identified essential items to include in PC research reports. A 2-round Delphi study identified a consensus list of items considered necessary. The CRISP Checklist contains 24 items that describe the research team，patients，study participants，health conditions，clinical encounters，care teams，interventions，study measures，settings of care，and implementation of findings/results in PC. Not every item applies to every study design or topic. The CRISP guidelines inform the design and reporting of（1）studies done by PC researchers，（2）studies done by other investigators in PC populations and settings，and（3）studies intended for application in PC practice. Improved reporting of the context of the clinical services and the process of research is critical to interpreting study findings/results and applying them to diverse populations and varied settings in PC.
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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 | 0.747 | 0.864 |
| Meta-epidemiology (narrow) | 0.004 | 0.009 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.013 | 0.024 |
| Research integrity | 0.019 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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".