Evolution of international collaborative research efforts to develop non-Cochrane systematic reviews
Bibliographic record
Abstract
This research-on-research study describes efforts to develop non-Cochrane systematic reviews (SRs) by analyzing demographical and time-course collaborations between international institutions using protocols registered in the International Prospective Register of Systematic Reviews (PROSPERO) or published in scientific journals. We have published an a priori protocol to develop this study. Protocols published in scientific journals were searched using the MEDLINE and Embase databases; the query terms "Systematic review" [Title] AND "protocol" [Title] were searched from February 2011 to December 2017. Protocols registered at PROSPERO during the same period were obtained by web scraping all non-Cochrane records with a Python script. After excluding protocols that had a fulfillment or duplication rate of less than 90%, they were classified as published "only in PROSPERO", "only in journals", or in "journals and PROSPERO". Results of data and metadata extraction using text mining processes were curated by two reviewers. These Datasets and R scripts are freely available to facilitate reproducibility. We obtained 20,814 protocols of non-Cochrane SRs. While "unique protocols" by reviewers' institutions from 60 countries were the most frequent, a median of 6 (2-150) institutions from 130 different countries were involved in the preparation of "collaborative protocols". The highest Ranked countries involved in overall protocol production were the UK, the U.S., Australia, Brazil, China, Canada, the Netherlands, Germany, and Italy. Most protocols were registered only in PROSPERO. However, the number of protocols published in scientific journals (924) or in both PROSPERO and journals (807) has increased over the last three years. Syst Rev and BMJ Open published more than half of the total protocols. While the more productive countries were involved in "unique" and "collaborative protocols", less productive countries only participated in "collaborative protocols" that were mainly published in PROSPERO. Our results suggest that, although most countries were involved in solitary production of protocols for non-Cochrane SRs during the study period, it would be useful to develop new strategies to promote international collaborations, especially with less productive countries.
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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 | 0.673 | 0.827 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.069 | 0.067 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.011 | 0.033 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".