Recommendations for reporting of systematic reviews and meta-analyses of diagnostic test accuracy: a systematic review
Bibliographic record
Abstract
BACKGROUND: This study is to perform a systematic review of existing guidance on quality of reporting and methodology for systematic reviews of diagnostic test accuracy (DTA) in order to compile a list of potential items that might be included in a reporting guideline for such reviews: Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA). METHODS: Study protocol published on EQUATOR website. Articles in full text or abstract form that reported on any aspect of reporting systematic reviews of diagnostic test accuracy were eligible for inclusion. We used the Ovid platform to search Ovid MEDLINE®, Ovid MEDLINE® In-Process & Other Non-Indexed Citations and Embase Classic+Embase through May 5, 2016. The Cochrane Methodology Register in the Cochrane Library (Wiley version) was also searched. Title and abstract screening followed by full-text screening of all search results was performed independently by two investigators. Guideline organization websites, published guidance statements, and the Cochrane Handbook for Diagnostic Test Accuracy were also searched. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Standards for Reporting Diagnostic Accuracy (STARD) were assessed independently by two investigators for relevant items. RESULTS: The literature searched yielded 6967 results; 386 were included after title and abstract screening and 203 after full-text screening. After reviewing the existing literature and guidance documents, a preliminary list of 64 items was compiled into the following categories: title (three items); introduction (two items); methods (35 items); results (13 items); discussion (nine items), and disclosure (two items). CONCLUSION: Items on the methods and reporting of DTA systematic reviews in the present systematic review will provide a basis for generating a PRISMA extension for DTA systematic reviews.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.386 | 0.677 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.040 | 0.032 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.018 | 0.009 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".