Using the GRADE‐ADOLOPMENT framework in developing the Philippine national screening guideline recommendations
Bibliographic record
Abstract
Abstract Background Preventive health screening is an important strategy in improving health outcomes of the general population and sustaining universal healthcare initiatives. Due to the large resource requirement, we used the GRADE ADOLOPMENT framework to synthesize the evidence and develop recommendations for preventive health screening. Objective Our objectives were to describe the development and feasibility of a national preventive screening practice guideline using the GRADE‐adolopment process and to discuss the methodological process, contextual differences from the source guidelines, and the resulting changes to the final recommendations. Methods A multidisciplinary team was convened. We used the GRADE‐ADOLOPMENT and Evidence‐to‐Decision (EtD) framework in synthesizing the evidence and developing the recommendations. Evidence from the World Health Organization, United States Preventive Services Task Force, and Canadian Task Force on Preventive Healthcare were used. Values and preferences of the guideline developers were incorporated through the EtD framework. Result In 6 months, we developed 16 evidence summaries and developed 23 recommendations for 16 prioritized conditions. Thirteen recommendations were adopted. Four recommendations were modified to address contextual differences. Six recommendations were developed de novo due to either lack of evidence or differences in values and preferences. Conclusion The GRADE‐adolopment framework was a feasible and efficient framework in adopting guidelines on preventive screening. The EtD framework improved the transparency and highlighted areas to consider in making recommendations for the Philippine context. Challenges encountered were insufficiency of local evidence, and the lack of experience and skills in interpreting and analyzing the evidence especially on screening strategies.
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 | 0.315 | 0.503 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.031 | 0.015 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".