Response
Bibliographic record
Abstract
We thank Wang and Yang for their letter relating to our article exploring the role of androgen receptor (AR) expression and outcomes in early breast cancer ( 1 ). We acknowledge some typographical errors in the text and in Table 1 of our article. In the study by Hu et al. ( 2 ), the total number of patients in the AR expression group was 1154 (not 1155 as reported). An in-depth check of our analysis confirmed that the correct number of patients was used to generate the odds ratio for that study, and, as such, results do not change. Wang and Yang also questioned how we extracted three-year survival data from the study by Hu et al. ( 2 ) who only reported five- and 10-year data. As described in the Methods section of our article, we either extracted data from text or estimated data from survival curves. For the Hu et al. ( 2 ) study, three-year survival data were estimated from the survival curve shown in Figure 1 of that article. Survival curves for disease-free survival (DFS) were not presented, and, as such, the Hu et al. ( 2 ) study was not included in our assessment of three-year DFS. We also acknowledge incorrect linking of the reference for the Park et al. study. The correct citation should be: Park S, Koo JS, Kim MS, et al. Androgen receptor expression is significantly associated with better outcomes in estrogen receptor–positive breast cancers. Ann Oncol . 2011;22(8): 1755–1762 ( 3 ). Finally, for the Luo et al. study ( 4 ), our analysis included both the triple-negative and non-triple-negative cohorts. We omitted details of the non-triple-negative cohort in Table 1 of our article. We thank Wang and Yang for their thorough evaluation of our article, and we will ensure that an erratum is issued to correct the above inconsistencies. We are confident that the analysis was robust and accurate and that the conclusion of our article is appropriately based on the results of our analysis.
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 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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.080 | 0.046 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".