An algorithm provided as initial guidance for reporting registry records and published protocols in systematic reviews
Bibliographic record
Abstract
OBJECTIVE: We aim to synthesize the available guidance with existing practices by Cochrane reviewers to generate an algorithm as a starting point in assisting reviewers reporting of registry records and published protocols (TRRs/PPs) use in systematic reviews of interventions. STUDY DESIGN: We used existing guidance from major review bodies, assessed the current reporting of TRRs/PPs use in a sample of Cochrane reviews, and engaged in critical analysis. Independent reviewers identified and extracted textual excerpts reporting the use of trial registry records and published protocols and codes following a systematic review framework. Based on these elements, and our initial research, we created an algorithm/graphical aid to visualize initial direction. RESULTS: We included 166 Cochrane systematic reviews published between August 2015 and 2016 from 48 review groups. Review authors' terminology (e.g., ongoing, terminated) varied between and within reviews. Reporting practices were diverse and inconsistent. CONCLUSIONS: This is a timely investigation in an era where evidence synthesis informs health and health care decisions. Our proposed algorithm provides initial direction to systematize the reporting of TRR/PP use. We hope that the algorithm generates further discussion to enhance the transparency of TRR/PP reporting and methodological research into the complexities of using protocols in systematic reviews of interventions.
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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 | 0.261 | 0.559 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.052 | 0.030 |
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".