Challenges and Opportunities in International Molecular Cancer Prevention Research: An ASPO Molecular Epidemiology and the Environment and International Cancer Prevention Interest Groups Report
Bibliographic record
Abstract
The International Agency for Research on Cancer estimates that over half of the new cancer cases and almost two-thirds of the cancer deaths in 2012 occurred in low and middle income countries. To discuss the challenges and opportunities to reducing the burden of cancer worldwide, the Molecular Epidemiology and the Environment and the International Issues in Cancer Special Interest Groups joined forces to hold a session during the 38th Annual Meeting of the American Society of Preventive Oncology (March 2014, Arlington, Virginia). The session highlighted three topics of particular interest to molecular cancer prevention researchers working internationally, specifically: 1) biomarkers in cancer research; 2) environmental exposures and cancer; and 3) molecular pathological epidemiology. A major factor for successful collaboration illuminated during the discussion was the need for strong, committed, and reliable international partners. A key element of establishing such relationships is to thoroughly involve individual international collaborators in the development of the research question; engaged international collaborators are particularly motivated to champion and shepherd the project through all necessary steps, including issues relating to institutional review boards, political sensitivity, laboratory-based assays, and tumor subtyping. Also essential is allotting time for the building, maintaining, and investing in such relationships so that successful international collaborations may take root and bloom. While there are many challenges inherent to international molecular cancer research, the opportunities for furthering the science and prevention of cancer worldwide are great, particularly at this time of increasing cancer incidence and prevalence in low and middle income countries.
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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 | 0.115 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.039 | 0.037 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.024 | 0.038 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".