Guidance was developed on how to write a plain language summary for diagnostic test accuracy reviews
Bibliographic record
Abstract
OBJECTIVE: To develop guidance for authors of diagnostic test accuracy (DTA) reviews to help them write a plain language summary of the results of their review. STUDY DESIGN AND SETTING: We used a combination of focus groups, user testing, and a web-based survey. Participants included patient representatives, media representatives, and health professionals. RESULTS: We present step-by-step guidance for authors of DTA reviews for writing a plain language summary. This guidance is illustrated with examples of reader-tested sentences, explanations, and a figure. CONCLUSION: We hope this guidance will allow reviewers to present the findings of DTA reviews so that it is easier for readers to understand the results and conclusions. This will increase the accessibility of these reviews for various audiences.
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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 | 0.214 | 0.568 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.064 | 0.058 |
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".