Symptoms and Cancer Outcome in Adjuvant Endocrine Therapy for Breast Cancer: Why Are They Associated?
Bibliographic record
Abstract
may predict or be associated with breast cancer outcome. It is tempting to speculate that patients who report more adverse effects are somehow getting higher levels or more effects from the endocrine drugsinvolved,bothontheirgeneralbodysystems(vasomotorsymptoms, musculoskeletal symptoms, vulvovaginal symptoms) and on any remaining breast cancer cells. However, something about these analyses does not quite fit with that conclusion. In both a previous paper by Cuzick et al in Lancet Oncology 3 and the current paper, 1 patients seem to have fewer recurrences if they have musculoskeletal symptoms in both the tamoxifen and the aromatase inhibitor arms, even though musculoskeletal symptoms are not a reported adverse effect of tamoxifen. Similar results are seen in an International Breast Cancer Study GroupB1to98report. 4 Why should this be true? These data suggest, as I postulated in my editorial 5 in relation to Cuzick’s article, that there may be something similar about patients who both report more adverse effects and have fewer recurrences. In Cuzick’s paper, counterintuitively, more adverse effects were associated with better compliance with endocrine therapy. In the current TamoxifenExemestaneAdjuvantMultinational(TEAM)trialpaper, 1 whiletheauthorsdonotreportcomplianceinthesamewayasCuzick did, they suggest that the adverse effect association with better outcomes persists despite treatment discontinuation. It may be that women who note and report their adverse effects in a more detailed wayareperhapsinturnalsomorelikelytopursueotherhealthyhabits
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".