Data for: Characterizing multi-criteria decision analysis, risk stratification, and hotspot analysis methods to optimise disease policymaking and evidence translation for medicines in low- and middle-income countries: A scoping review
Bibliographic record
Abstract
The supplementary materials include the Preferred Reporting Items for Systematic Reviews and Meta-Analyses -S1 PRISMA checklist, which provides a structured set of sections for reporting the scoping review and contains key information from the paper, including the aims, specific methods, data sources, analyses, and how each item is addressed. It contains three appendices corresponding to each main section of the paper, as well as a list of supporting tables and figures. S1 Appendix includes data related to key attributes, such as the number of articles over time, geographical region and country, author affiliation, disease focus, and stage of medicine translation. S2 Appendix includes data on the specific analytical methods presented in the review—multi-decision criteria analysis (MDCA), risk stratification, and hotspot analysis—such as article summaries, variables, and visual tools. S3 Appendix includes data related to the grey literature, including the search strategies and outputs from international and regional organisations, conferences, and independent journals. S1 PRISMA-Checklist S1 Appendix. Tables and figures related to the section 'Paper and report attributes' S2 Appendix. Tables and figures related to the section 'Data analytical methods' S3 Appendix. Tables and figures related to the section 'Grey literature'
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.312 | 0.060 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".