The BIGG-REC database makes available all WHO/PAHO evidence informed recommendations
Bibliographic record
Abstract
OBJECTIVES: To build and maintain a living database of the Pan American Health Organization/World Health Organization (PAHO/WHO) recommendations developed using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). STUDY DESIGN AND SETTING: Guidelines are identified from WHO and PAHO databases. We periodically extract recommendations, according to the health and well-being targets of sustainable development goal 3 (SDG-3). RESULTS: As of March 2022, the International database of GRADE guidelines (https://bigg-rec.bvsalud.org/en) database hosted 2,682 recommendations contained in 285 WHO/PAHO guidelines. Recommendations were classified as follows: communicable diseases (1,581), children's health (1,182), universal health (1,171), sexual and reproductive health (910), noncommunicable diseases (677), maternal health (654), COVID-19 (224), use of psychoactive substances (99), tobacco (14) and road and traffic accidents (16). International database of GRADE guidelines allows searching by SDG-3, condition or disease, type of intervention, institution, year of publication, and age. CONCLUSION: Recommendation maps provide an important resource for health professionals, organizations and member states that use evidence-informed guidance to make better decisions, providing a source for the adoption or adaptation of recommendations to meet their needs. This one-stop shop database of evidence-informed recommendations built with intuitive functionalities undoubtedly represents a long-needed tool for decision-makers, guideline developers, and the public at large.
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 | 0.020 | 0.168 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.045 | 0.042 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.169 | 0.069 |
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".