Development of a topic‐specific bibliographic database supporting the updates of SPIRIT 2013 and CONSORT 2010
Bibliographic record
Abstract
Introduction: An important mechanism of research waste is inadequate incorporation of, and references to, previous relevant research. Identifying references for a research manuscript can be challenging, in part due to the exponential rise in potentially relevant literature to consider. For large research projects, such as developing or updating reporting guidelines, it may be helpful to construct a supportive topic-specific bibliographic database. Methods: In support of updating the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 and the CONsolidated Standards Of Reporting Trials (CONSORT) 2010, we developed the SPIRIT-CONSORT Evidence Bibliographic database (SCEBdb): a freely available topic-specific bibliographic database of publications providing an evidence foundation for the updates. We searched multiple sources of potential publications and tagged included ones with database-specific keywords. For context, we also formulated 10 core considerations for constructing topic-specific bibliographic databases and identified and described 5 illustrative other databases. Results: As of April 2024, the SCEBdb included 846 publications. The database proved useful as a supplementary information source for our scoping review of published comments on SPIRIT 2013 and CONSORT 2010, for a supplementary Delphi process, and in the writing phase of the guidance documents. We expect that the database will be useful for future projects within the fields of clinical research methodology, bias, evidence synthesis, and randomized trials. Conclusion: The methods involved in constructing the SCEBdb, and our suggested core considerations for topic-specific bibliographic databases, could be helpful for researchers reflecting on whether, and how, to develop a topic-specific bibliographic database.
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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 | 0.378 | 0.648 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.115 | 0.113 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.092 | 0.050 |
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".