Abstract LB-52: Relentless in the fight against blood cancer: the Leukemia & Lymphoma Society
Bibliographic record
Abstract
Abstract WHO WE ARE: The Leukemia & Lymphoma Society (LLS) is the world's largest voluntary health organization dedicated to funding blood cancer research and providing education and patient services. Founded in 1949, we are relentless in pursuit of our mission: Cure leukemia, lymphoma, Hodgkin's disease and myeloma, and improve the quality of life of patients and their families. WHAT WE DO: Investing in blood cancer research: LLS has invested more than $814 million in research, approximately $76.6 million in fiscal year 2011 alone. Programs like the Specialized Center of Research (SCOR), and our Translational Research Program have directly contributed to many breakthrough cancer treatments. Research funded by LLS has led or contributed to advances such as chemotherapy, bone marrow and stem cell transplantation and new, targeted oral therapies. Providing critical information and support for patients and their families: We made 7.1 million contacts with patients, caregivers and healthcare professionals in fiscal year 2011, Advocating for issues impacting blood cancer patients: With more than 56,000 advocacy volunteers throughout the country, our voice is being heard by those responsible for legislation to fund blood cancer research and educational programs. WHY: An estimated 1,012,533 people in the United States are living with, or are in remission from, leukemia, Hodgkin lymphoma, non-Hodgkin lymphoma or myeloma.Approximately every four minutes, someone new is diagnosed with blood cancer. Approximately every 10 minutes, someone dies. Leukemia causes more deaths than any other cancer among children, adolescents and young adults under the age of 20. Lymphomas are the most common blood cancers and incidence increases with age. The survival rate for myeloma is only 41.1 percent. Incidence is more than twice as high among African Americans as for all other races. WHO WE SERVE: In addition to our national headquarters in White Plains, NY, we have a network of 59 local chapters across the United States and Canada. Our constituents live in urban, suburban, and rural communities, seek treatment at large comprehensive cancer centers and community clinics, and represent cultures from around the world. We work in partnership with community organizations, treatment centers, and government agencies to deliver quality cancer support services and to reach as many patients as we can in our region. We also work in partnership with other oncology and health organizations to meets the needs of the underserved people within our community. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-52. doi:1538-7445.AM2012-LB-52
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.112 | 0.064 |
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".