Abstract P2-09-05: ICRP analysis: Obesity research in breast cancer
Bibliographic record
Abstract
Abstract The International Cancer Research Partnership (ICRP) is an alliance of governmental and charitable organizations from the USA, Canada, Europe, Australia and Japan, funding regional, national and international cancer research grants and awards. One key activity of the partnership is the ICRP database of information about member’s funded grant projects (N>60,000 grants, from 80 members, totaling $13.6 billion USD). Each project is coded to a Common Scientific Outline (CSO) classification, a classification system of broad areas of cancer research. Obesity has been associated with an increased risk of developing several cancer types, including breast cancer. Worldwide, obesity rates have nearly doubled since 1980 (WHO), and there is significant concern that rising rates of obesity will result in additional obesity-related cancer incidence. Breast cancer is the most common cancer in women worldwide and its incidence has risen in most countries in the last 30 years.1 In addition, there is convincing research evidence that body fatness is linked to breast cancer incidence (postmenopause).2 With this in mind, the ICRP has analyzed obesity-related breast cancer research in its portfolio over three time periods: 2006, 2008 and 2010. Methods: Using a combination of keyword searches and manual review, a total of over 1040 awards over the period 2006-2010 were found in the ICRP portfolio that were related to obesity and cancer. Of these, 353 awards were considered to be relevant to breast cancer (relevance ≥25%). These were assessed by Common Scientific Outline (CSO) areas.3 Results: The numbers of obesity-relevant awards and research investment were higher for breast cancer than for any other cancer type in the ICRP portfolio from 2006 to 2010. Research was being conducted across all CSO areas, from basic biology, etiology, prevention, early diagnosis/prognosis to treatment and cancer survivorship. Between 2006 and 2010, there was a slight decrease in etiology research (CSO2), and an increase in research into cancer survivorship (CSO6). It is notable that training awards are increasing, indicating that the research organizations contributing data to this analysis consider workforce training to be a priority area. Conclusion: We were able to use the ICRP database to identify trends in funded grant projects related to obesity research and breast cancer. Despite increased numbers of awards, the overall stasis in research funding over this period, and the decline in investment in etiology is concerning. As breast cancer incidence continues to increase, research efforts to understand the causes of increased incidence are essential. Further research investment in these areas may be required. 1 http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx (accessed 7th January 2014) 2 World Cancer Research Fund / American Institute for Cancer Research. Continuous Update Project Report. Food, Nutrition, Physical Activity and the Prevention of Breast Cancer, 2010 3 https://www.icrpartnership.org/CSO.cfm Citation Format: Kari Wojtanik, Rhonda Aizenberg, Susan Higginbotham, Lynne Davies. ICRP analysis: Obesity research in breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-09-05.
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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 | 0.025 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.053 | 0.058 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".