The TARCiS statement: Guidance on terminology, application, and reporting of citation searching
Bibliographic record
Abstract
ABSTRACT Evidence syntheses adhering to systematic literature searching techniques are a cornerstone of evidence-based health care. Beyond term-based searching in electronic databases, citation searching is a prevalent search technique to identify relevant sources of evidence. However, for decades, citation searching methodology and terminology has not been standardized. We performed an evidence-guided four-round Delphi consensus study with 27 international methodological experts in order to develop the Terminology, Application, and Reporting of Citation Searching (TARCiS) statement. TARCiS comprises ten specific recommendations on when and how to conduct and report citation searching in the context of systematic literature searches and four research priorities. We encourage systematic reviewers and information specialists to incorporate TARCiS into their standardized workflows.
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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 | 0.664 | 0.857 |
| Meta-epidemiology (narrow) | 0.007 | 0.011 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.059 | 0.045 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.016 | 0.033 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.043 | 0.045 |
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".