Response: Re: Ki67 Index, HER2 Status, and Prognosis of Patients With Luminal B Breast Cancer
Bibliographic record
Abstract
Howell et al. are interested in using our dataset to address a different but related hypothesis to the one addressed in our recent article, which concerned the value of Ki67 index as a proliferation marker in the context of the intrinsic subtype approach to breast cancer risk assessment. A response to their hypothesis requires quantitative estrogen receptor (ER) and progesterone receptor data for more than 4000 tissue samples in tissue microarrays. Our study on automated quantitative ER assessment ( 1 ) did not reveal evidence that additional prognostic information could be extracted from quantitative ER beyond that already captured by a binary cut point set at approximately 1%. Currently, our dataset has captured complete immunohistochemical information on progesterone receptor in a semiquantitative fashion around visually assessed cut points rather than as a quantitative continuous variable. An extensive reanalysis of the primary image data for ER (possibly by using an improved image analysis approach) and progesterone receptor in a quantitative fashion would, therefore, be necessary to address their inquiry. Our primary image data have been captured, have been published ( 2 , 3 ), and are publicly accessible at http://www.gpecimage.ubc.ca/tma/web/viewer.php . We have therefore invited Howell et al. to access these published digital images and generate the quantitative ER and progesterone receptor scores by whatever system that they feel is appropriate. We are also willing to consider collaborations with other academic groups who wish to conduct similar exercises. We will then test the hypothesis that quantitative hormone receptor data add useful prognostic information in multivariable models incorporating human epidermal growth factor receptor 2 (HER2) and Ki67 data.
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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 | 0.005 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.176 | 0.051 |
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".