Living Rapid Research Needs Appraisal (RRNA) for Priority Diseases Protocol
Bibliographic record
Abstract
Systematic reviews require time and resources to conduct that might not be available in a rapidly developing context such as during new and (re-) emerging infectious disease outbreaks. As part of the Pandemic-PACT program at the Pandemic Sciences Institute at the University of Oxford we have, in collaboration with Cochrane Response and Cochrane South Africa, developed a "living" Rapid Research Needs Appraisal (RRNA) methodology. The aim is to develop a protocol for rapidly and robustly identify key evidence gaps across pre-defined research domains to inform research prioritisation and coordination in preparedness time and during outbreaks. The RRNA utilises modified systematic review methods, a global relay of reviewers, and systematic review software. The objective is to strengthen capability for carrying out RRNAs, and establish a baseline evidence platform for priority infectious diseases that can be readily updated as new evidence emerges, utilising an adaptable "living" RRNA protocol. This data will be of relevance to funders, researchers, clinical and public health stakeholders involved in management of infectious diseases of epidemic and pandemic potential.
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 | 0.354 | 0.550 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.156 | 0.036 |
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".