Contextual differences considered in the Tunisian ADOLOPMENT of the European guidelines on breast cancer screening
Bibliographic record
Abstract
BACKGROUND: Breast cancer is a common disease in Tunisia and is associated with high mortality rates. The "Instance Nationale de l'Evaluation et de l'Accréditation en Santé" (INEAS) and the Tunisian Society of Oncology decided to develop practice guidelines on the subject. While the development of de novo guidelines on breast cancer screening is a demanding process, guideline adaptation appears more appropriate and context sensitive. The objective of this paper is to describe the adaptation process of the European Guidelines on Breast Cancer Screening and Diagnosis to the Tunisian setting in terms of the methodological process, contextual differences between the source and adoloped guideline, and changes in the recommendations. METHODS: We used the 'Grading of Recommendations Assessment, Development and Evaluation' (GRADE)-ADOLOPMENT methodology to prioritize the topic, select the source guideline, and prioritize the questions and the outcomes. Once the source guideline was selected-the European Breast Cancer Guidelines-the European Commission´s Joint Research Centre shared with the project team in Tunisia all relevant documents and files. In parallel, the project team searched for local studies on the disease prevalence, associated outcomes' baseline risks, patients' values and preferences, cost, cost-effectiveness, acceptability, and feasibility. Then, the adoloping panel reviewed the GRADE evidence tables and the Evidence to Decision tables and discussed whether their own judgments were consistent with those from the source guideline or not. They based their judgments on the evidence on health effects, the contextual evidence, and their own experiences. RESULTS: The most relevant contextual differences between the source and adoloped guidelines were related to the perspective, scope, prioritized questions, rating of outcome importance, baseline risks, and indirectness of the evidence. The ADOLOPMENT process resulted in keeping 5 out of 6 recommendations unmodified. One recommendation addressing "screening versus no screening with ultrasound in women with high breast density on mammography screening" was modified from 'conditional against' to 'conditional for either' due to more favorable ratings by the adoloping panel in terms of equity and feasibility. CONCLUSION: This process illustrates both the feasibility of GRADE-ADOLOPMENT approach and the importance of consideration of contextual evidence. It also highlights the value of collaboration with the organization that developed the source guideline.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.264 | 0.405 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".