Tamoxifen in Breast Cancer: Symptom Reporting
Bibliographic record
Abstract
Clinical studies have traditionally identified treatment-specific side effects by comparison of voiced side effects in treatment and placebo arms of a study. Highly motivated women in a clinical trial may underreport drug-induced symptoms for medications which may be considered lifesaving. Affective symptoms during treatment of early breast cancer with tamoxifen (an estradiol receptor antagonist) were reported as infrequent by the manufacturer. However, reports suggest a higher rate of depression during general use. The objective of the present study was to examine the frequency of symptoms that might be side effects of tamoxifen and to relate them to the way the women attributed such symptoms. The exploratory study involved semistructured telephone interviews of 25 women who were taking tamoxifen. Textual analysis of the information was used to examine the symptoms described by the women. They were also asked whether any symptoms were related to the medication. The symptoms and their attribution were evaluated against a background of self-perceived stress. The principal finding was a pattern of ambivalence in attributing symptoms to the drug. Of all the symptomatic changes noted, the women only attributed 51% to tamoxifen. Flushes, fatigue, and depression were reported most frequently during treatment; flushes were readily attributed to tamoxifen but depression and fatigue were attributed to another factor by half of the symptomatic women. Women who reported moderate to high levels of life stress were less likely to attribute symptoms to drug therapy. The results suggest that women taking tamoxifen may not attribute known drug side effects to use of the medication.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".