076 Enhancing the Acceptance and Implementation of GRADE Summary Tables for Evidence about Diagnostic Tests
Bibliographic record
Abstract
Background The GRADE Working Group developed Summary tables adapted to summarise and present evidence from diagnostic test accuracy (DTA) systematic reviews. Objective To develop guidance on what information to include in these summary tables and to determine the best method(s) for presentation for different end users, including healthcare providers, systematic reviewers and guideline developers. Methods We presented a number of alternative summary tables to participants. We conducted questionnaires and one-on-one user testing interviews with target end users. We presented printed copies of summary tables and asked open-ended and 7-point Likert-scale questions to obtain information about users’ understanding and preferences. Results All participants (n = 60) agreed that using summary tables to present results of DTA reviews is helpful. Presentation of several disease prevalence values was identified as a source of confusion. There was an overall preference for placement of sensitivity and specificity values inside summary tables to allow making a link to individual test results (TP, FN, TN, FP). A third of the participants read explanatory content in table footnotes. Two thirds of the participants noted that additional data, including adverse effects, costs, and treatment consequences, would be helpful for making appropriate conclusions and decisions about diagnostic tests. Discussion As results of DTA reviews are conceptually complicated, presenting the data in a clear, comprehensive, comprehensible way that is tailored to different end users is critical. Implications for Guideline Developers/Users We are developing a 3-layer approach, with varied content in summary tables of each layer tailored to the needs of different end users.
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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 | 0.576 | 0.812 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.022 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".