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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".