Is tamoxifen prevention in younger women underutilized? Analysis of competing causes of mortality
Bibliographic record
Abstract
647 Background: Our past work on Tamoxifen impacting overall mortality outcome among survivors of the 1st breast cancer (JCO, 1998, 16:2018–24) introduced methodology permitting analyses of competing cause mortality, adjustable as new data emerge. This analysis estimates Tamoxifen impact on four conditions among BrCa survivors known to be affected by Tamoxifen, i.e. contralateral breast cancer; uterine cancer, cardiovascular (CVS) mortality, and thromboembolism (TE). Updated RR rates of Tamoxifen impacting the four conditions were obtained from recent literature (Lancet, 2005, 365:1687–1717; JNCI,2005, 97:1652–62; 97:1609–10). Methods: The effect of Tamoxifen was calculated by applying Relative Risk Rates (RR) to the respective underlying Canadian age-specific mortality figures, separately for each condition. The final mortality impact was the “net mortality”, defined as differences of deaths, from each condition, between 1,000 Tamoxifen users vs 1,000 non-users (Dif N/1,000), expressed for ages <50 and >50. (i.e sum of avoidances [-] vs hazards[+]) Results: see Table below. Conclusion: Our results show a favorable Tamoxifen impact on overall mortality, particularly evident for women age <50, in whom the breast cancer mortality reduction significantly outweighs the hazards. These data are applicable to Tamoxifen prevention in population-women who are at high risk for BrCa. Their outcome profiles affected by Tamoxifen mirror those of the survivors of the 1st BrCa. While for women >50, other chemoprevention alternatives exist (i.e. trials of Aromatase Inhibitors), for younger women no chemoprevention trials are presently in existence. Our data indicate that in those, routine use of Tamoxifen may save substantial numbers of lives. [Table: see text] No significant financial relationships to disclose.
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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".