Decision-Making about Healthcare Related Tests and Diagnostic Strategies: User Testing of GRADE Evidence Tables
Bibliographic record
Abstract
OBJECTIVE: To develop guidance on what information to include and how to present it in tables summarizing the evidence from systematic reviews of test accuracy following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. METHODS: To design and refine the evidence tables, we used an iterative process based on the analysis of data from four rounds of discussions, feedback and user testing. During the final round, we conducted one-on-one user testing with target end users. We presented a number of alternative formats of evidence tables to participants and obtained information about users' understanding and preferences. RESULTS: More than 150 users participated in initial discussions and provided their formal and informal feedback. 20 users completed one-on-one user testing interviews. Almost all participants preferred summarizing the results of systematic reviews of test accuracy in tabular format rather than plain text. Users generally preferred less complex tables but found presenting sensitivity and specificity estimates only as too simplistic. Users found the presentation of test accuracy for several values of prevalence initially confusing but modifying table layout and adding sample clinical scenarios for each prevalence reduced this confusion. Providing information about clinical consequences of testing result was viewed as not feasible for authors of systematic reviews. CONCLUSION: We present the current formats for tables presenting test accuracy following the GRADE approach. These tables can be developed using GRADEpro guidelines development tool (www.guidelinedevelopment.org or www.gradepro.org) and are being further developed into electronic interactive tables that will suit the needs of different end users. The formatting of these tables, and how they influence result interpretation and decision-making will be further evaluated in a randomized trial.
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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 | 0.594 | 0.866 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".