Merit Award Presented at the Breast Cancer Symposium
Bibliographic record
Abstract
Nine Conquer Cancer Foundation of the American Society of Clinical Oncology Merit Awards in Breast Cancer have been awarded for 2015. The awards, which were given at the Breast Cancer Symposium, recognize young investigators for their valuable research contributions to their areas of study and their commitment to advancing cancer care. The award recipients were selected based on the scientific merit of their abstracts. The award recipients are: Sahaja Acharya, MD, of Washington University School of Medicine, for “Distance to nearest radiation facility and treatment choice in early stage breast cancer” (Abstract 73); Faith Aydogan, MD, of the Department of Surgery at Brigham and Women's Hospital and Dana-Farber Cancer Institute, for “Tumor subtype and race in male breast cancer: A population-based cohort study” (Abstract 149); Andrea Covelli, MD, PhD, of University of Toronto, for “A misperceived threat: Understanding the increasing mastectomy rates” (Abstract 75); Clark DuMontier, MD, of Boston University Medical Center, for “Motivation and mortality in geriatric patients with early stage breast cancer” (Abstract 105); Saima Hassan, MD, PhD, of Oregon Health & Science University, for “Biological indicators of response and resistance to PARP inhibition in BRCA wild-type breast cancer” (Abstract 125); Kenneth Kehl, MD, of The University of Texas MD Anderson Cancer Center, for “Trends in BRCA1/2 mutation testing rates among young women and men with breast cancer, 2005-2012” (Abstract 103); Maryam Nemati Shafee, MD, of The University of Texas MD Anderson Cancer Center, for “Aromatase inhibitors and the risk of contralateral breast cancer in BRCA mutation carriers” (Abstract 3); Talha Shaikh, MD, of Fox Chase Cancer Center, for “Impact of margin status and re-excision on local control in patients undergoing breast-conservation therapy for ductal carcinoma in situ” (Abstract 57); and Sean Szeja, MD, of Sunnybrook Odette Cancer Centre, for “Outcomes associated with adjuvant radiation after lumpectomy for elderly women with T1-2N0M0 triple-negative breast cancer: SEER analysis” (Abstract 39).
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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 teacher head, 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".