Bibliographic record
Abstract
to recurrence-this is seen in the curves in Figure 3 1 (the lowrisk curves are flat and the high-risk curves are peaked).We propose that the annual risk of reactivation is attenuated by tamoxifen.In the high-risk group, use of tamoxifen delays the reactivation of cancers, but at the end of active treatment, more residual dormant cancers are present in the metastatic niche than in the untreated group.We predict that after tamoxifen cessation, the dormant cancers will slowly emerge from quiescence, and we will observe a switch from tamoxifen being a protective factor to a risk factor in the later years of follow-up.2,3 In fact, this switch was seen for luminal B tumor subtypes after 10 to 15 years (eTable 2 in the Supplement 1 ).The phenomenon is illustrated by reference to luminal A vs luminal B tumor subtypes and is a manifestation of a universal property of breast cancer in which the distribution of times to recurrence can be predicted by the cumulative risk of recurrence for any defined subgroup through the agency of reactivation from tumor dormancy.2,3
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 | 0.005 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.397 | 0.460 |
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".