Using the Grade Approach to Support the Development of Recommendations for Public Health Interventions in Radiation Emergencies
Bibliographic record
Abstract
The World Health Organization (WHO) guideline development policy requires that WHO guidelines be developed in a manner that is transparent and based on all available evidences, which must be synthesised and formally assessed for quality. To fulfil this requirement, the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach of rating quality of evidence and grading strength of recommendations was applied when developing the WHO recommendations on public health interventions in radiation emergencies. The guideline development group (GDG) formulated 10 PICO (P: population; I: intervention; C: comparator; O: outcomes) questions to guide the development of recommendations on response interventions during the early/intermediate and late emergency phases and on risk communications for mitigating psycho-social impact of radiation emergencies. For each PICO question, an extensive evidence search and systematic review was conducted. The GDG then formulated the recommendations using the evidence to recommendation (E-2-R) decision-making matrix and evaluated the strength of each recommendation.
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 | 0.345 | 0.663 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.066 | 0.035 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".