Metadata record for the article: Evaluation of multiple transcriptomic gene risk signatures in male breast cancer
Bibliographic record
Abstract
Summary This metadata record provides details of the data supporting the claims of the related article: “Evaluation of multiple transcriptomic gene risk signatures in male breast cancer”. The related study presents the gene expression results of 381 M0, ER+ve, HER2-ve male male breast cancer (BCa) patients enrolled in the Part 1 (retrospective analysis) of the International Male Breast Cancer Program, as described in https://doi.org/10.1093/annonc/mdx651. Type of data: survival analysis Subject of data: Homo sapiens Sample size: 381 Population characteristics: EORTC/TBCRC/BIG/NABCG International Male Breast Cancer Program enrolled male patients with histologically proven BCa, diagnosed between 1990 and 2010, across multiple participating institutions. Data access The data underlying the Kaplan Meier survival curves and tables that support the findings of this study are available from European Organisation for the Research and Treatment of Cancer (EORTC). However, the data are not publicly available and restrictions apply to their availability as they were used under license from EORTC for the current study. Data can be made available with the permission of EORTC. Data enquiries can be made to the corresponding author, and data requests can be made at https://www.eortc.org/data-sharing/. Corresponding author(s) for this study John M. S. Bartlett. Ontario Institute for Cancer Research, Diagnostic Development, MaRS Centre, 661 University Avenue, Suite 510, Toronto, ON M5G 0A3, Canada. 647-259-4251. John.Bartlett@oicr.on.ca Study approval Ethics approval was provided by the University of Toronto (#30035).
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.585 | 0.233 |
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".