Bibliographic record
Abstract
To the Editor: We read the article by Antoniou et al. (August 7 issue)1 because we like to know the penetrance of breast-cancer genes before we recommend surgery. After reading the article, we are unsure of what to tell patients. The simplest message would be that the risk up to 70 years of age is 35%, but this is based on an analysis of patients who received a diagnosis of breast cancer from 1930 to the present. The relevant risk value is that for a woman without breast cancer who is found to have a PALB2 mutation today. Most patients in consultation for breast cancer at Women’s College Hospital were born in 1960 or later. Is their risk of the disease really 6.3 times as high as the risk among women born before 1940 (Table 3 of the article), and how does this translate into a lifetime risk? It is remarkable that the penetrance could increase by a factor of 6 in only 20 years. Personalized medicine is a fine idea, but it is not helpful to be told that “no single set of penetrance estimates applies to all PALB2 mutation carriers” without any clues about how to calculate risk.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".