Predictors of patient-reported toxicities from endocrine therapy: Importance of illness perceptions, treatment beliefs, and fear of recurrence.
Bibliographic record
Abstract
542 Background: Numerous studies have documented the toxicities of endocrine therapy (ET) for early breast cancer (EBC) and their deleterious impact on quality of life and adherence. However, little is known about the factors that underlie patient's susceptibility to report toxicities. The identification of risk factors for toxicities from ET is important as it would allow early targeting of symptom management interventions for women more vulnerable to adverse effects of ET. This prospective study aims to examine the impact of pre-treatment perceptions of EBC, ET beliefs and fear of breast cancer (BC) recurrence (FBCR) on toxicities reported after 6 months of ET. Methods: Women diagnosed with EBC completed a survey prior to initiating endocrine therapy, then at 3, 6 and 12 months. Standardized self-report instruments were used to assess EBC perceptions, ET beliefs, FBCR and toxicities. Clinical and treatment variables were also evaluated. Univariate analyses and mulitivariate regression were conducted to identify factors associated (p<0.1) with side effects at 6 months. Results: Since 9/2010, 173 patients have consented and 84 (mean age = 60 y) have completed the questionnaires at baseline and after 6 months of ET. Controlling for age, none of the clinical or treatment variables (stage of disease, type of surgery, receipt of chemotherapy and radiation therapy) were significant univariate predictors of toxicities. In multiple regression, stronger perceptions that BC has serious consequences on their lives (β=0.218, p<0.05), greater concerns about the adverse effects of ET (β=0.215, p<0.05) and higher levels of FBCR (β=0.316, p<0.01) at baseline were associated with higher levels of reported toxicities. Conclusions: Baseline psychological factors predicted level of patient-reported toxicities to a larger extent than clinical/treatment factors. How patients perceived their illness, their beliefs about ET side effects and their fear of cancer recurrence are strongly associated with side effects experienced after 6 months of ET. These results could facilitate the identification of a subgroup of patients for early interventions to improve symptom management.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".