Tamoxifen versus aromatase inhibitors for the adjuvant treatment of breast cancer in older women: A feasibility study to examine differences in cognitive outcomes.
Bibliographic record
Abstract
TPS626 Background: Tamoxifen (TAM) and aromatase inhibitors (AIs) are the mainstay endocrine therapies (ETs) for the treatment of hormone-receptor-positive (HR+) breast cancer (BC). While these ETs dramatically improve BC outcomes by reducing estrogen activity, increased awareness of the neuroprotective effects of estrogen on cognition has led to concerns regarding potential adverse cognitive effects. Cognitive concerns are heightened by the fact BC is more common among older women, who are already at greater risk of cognitive impairment (CI). While the anti-estrogen properties of TAM and AIs are unique, their differential effect on cognition remains unknown. Our overarching objective is to compare the cognitive effects of TAM and AIs in older women; however, before undertaking the large multicenter study needed to meet this goal, we are conducting a feasibility study to i) establish metrics on participation, retention, and ET adherence, and ii) inform sample size calculation for the future multicenter study. This feasibility study is designed such that all data can be rolled into the future study. Methods: Our convenience sample will consist of chemotherapy-naïve women aged 60+, prescribed TAM (n = 25) or an AI (anastrozole or letrozole; n = 50) in the adjuvant setting for a first diagnosis of HR+ BC. Consenting patients are invited to undergo baseline assessment before ET initiation and follow-up assessment after one year of treatment. Participation and retention rates will be established. ET adherence rates will be ascertained using monthly telephone self-reports. Since data from the current feasibility study will be rolled into the future multicenter study, baseline and follow-up assessments include a neuropsychological battery designed according to International Cognition and Cancer Task Force guidelines, as well as measures of mood, pain, and fatigue so that these factors may be controlled for in the future study. Differences in the proportion of women in each ET group who develop CI over the year will be used to establish parameter estimates for sample size calculation. Recruitment began in July 2015, and 40 participants have been enrolled to date. Clinical trial information: NCT02480218.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".