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Abstract P2-09-05: ICRP analysis: Obesity research in breast cancer

2015· article· en· W1655918213 on OpenAlexaboutno aff
Kari Wojtanik, Rhonda Aizenberg, Susan Higginbotham, Lynne Davies

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerObesityMedicineCancerIncidence (geometry)General partnershipEnvironmental healthInternal medicineBusinessFinance

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.274
GPT teacher head0.522
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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