Facilitating Prospective Registration of Diagnostic Accuracy Studies: A STARD Initiative
Bibliographic record
Abstract
Although the introduction of prospective trial registration policies has been successful in reducing waste in research, diagnostic accuracy studies are rarely registered. We describe why diagnostic accuracy studies should be registered, and where and how this can be done. Advantages of registration include the identification of unpublished studies, prevention of selective outcome reporting, prevention of unnecessary duplication of research, collaboration between researchers, and linkage of study materials. In a survey among representatives of 16 major trial registries, such as ClinicalTrials. gov, ANZCTR (Australian New Zealand Clinical Trials Registry), and the UK-based ISRCTN registry (International Standard Randomised Controlled Trial Number), 13 responded, of which 8 (62%) indicated they always accept registration of diagnostic accuracy studies and 5 (38%) do so in some cases. However, all but one of them (92%) indicated that their registry currently does not provide specific guidance for registering diagnostic accuracy studies. A second survey among the 85 members of the STARD Group (Standards for Reporting Diagnostic Accuracy) resulted in the identification of 14 essential protocol items and was used for developing a guide on how these items can be registered in existing major trial registries. We propose that investigators responsible for diagnostic accuracy studies should register their study, before recruiting patients, in 1 of the existing major trial registries that are willing to host such studies. We also propose that governmental, research, and academic institutions that provide funding for and journals that publish diagnostic accuracy studies require such registration. (C) 2017 American Association for Clinical Chemistry
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.924 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".