PD45 A Rapid Evidence Synthesis Method For Cancer Screening Recommendations In A Hospital Setting
Bibliographic record
Abstract
Introduction International agencies advocate for population-based cancer screening to prevent cancer-related deaths. The Arturo Lopez Perez Oncology Institute is interested in implementing screening programs, but international recommendations differ on program details such as screening tests, target population, age range, and frequency. A review of international evidence-based recommendations is essential for advising stakeholders on the effective implementation of screening programs. Methods A rapid scoping review was performed to identify international recommendations on cancer screening programs. Evidence-based recommendations derived from the World Health Organization and the European Union were analyzed. We also searched for evidence-based recommendations from the following health technology assessment agencies with specific sections for evaluating screening strategies: the Canadian Agency for Drugs and Technologies in Health (Canada), the Institute for Quality and Efficiency in Health Care (Germany), the Medical Services Advisory Committee (Australia), and the National Institute of Health and Care Excellence (UK). Additionally, we explored international cancer screening programs implemented by health systems in the aforementioned countries or in countries with implemented screening programs. Finally, we searched for recommendations from scientific societies on cancer screening strategies. This iterative process was repeated for five different cancers. Results We found a total of 32 favorable or unfavorable recommendations for breast, cervical, colorectal, lung, gastric, and prostate cancer screening. Breast and cervical cancer had the highest number of favorable recommendations, with complete agreement on the type of test and only small differences regarding age range and periodicity. On the other hand, we found some recommendations against population-based screening for prostate and gastric cancer and limited agreement for both test type and target population. Direct comparisons between the recommendations served as a guide to elaborate a cancer screening program based on the most recommended strategies. Conclusions This rapid scoping review allowed us to assess the consistency of cancer screening recommendations. Major differences were found mainly between recommendations from international agencies and scientific societies. As a result, a cancer screening program was designed based on the most recommended strategies.
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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 | 0.215 | 0.568 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.043 | 0.036 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.126 | 0.014 |
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".