Systematic reviews of diagnostic test accuracy: Methodology, reporting, and imaging specific issues
Bibliographic record
Abstract
Medical imaging is an integral part of clinical practice, with diagnostic test accuracy (DTA) representing a key consideration guiding test selection. DTA research can inform the utilization of diagnostic tests, however methodologic shortcomings and incomplete reporting may introduce bias and limit assessment of clinical applicability. This thesis aims to illustrate best practices for DTA systematic reviews and highlight on issues specific to imaging reviews. Regarding methodology of imaging DTA systematic reviews, we found that a minority of DTA systematic reviews published in imaging journals had used recommended statistical methods and less than a tenth of reviews reported how they handled multiple index test readers; both of which are important considerations for imaging systematic reviews to avoid overestimating test accuracy and ensure clinical applicability. Regarding overinterpretation practices in imaging DTA systematic reviews, we found a majority of reviews contained overinterpretation practices or ‘spin’ in both the abstract and full text. Reviews published in high impact factor journals were less likely to contain overinterpretation practices, however, this difference did not persist when a sensitivity analysis excluding Cochrane systematic reviews was performed. To assist in optimal reporting of DTA systematic reviews, we compiled a list of items potentially applicable to a reporting guideline for DTA systematic reviews, PRISMA-DTA. These items formed the basis of the Delphi survey which led to the PRISMA-DTA checklist. Lastly we provide a narrative review providing guidance and illustrative examples of current best practices for methodology and reporting of DTA systematic reviews.
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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 | 0.691 | 0.924 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.028 | 0.035 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".