The Effects of Pharmacological and Non-Pharmacological Interventions on Symptom Management and Quality of Life among Breast Cancer Survivors Undergoing Adjuvant Endocrine Therapy: A Systematic Review
Bibliographic record
Abstract
Breast cancer survivors need to undergo adjuvant endocrine therapy after completion of curative treatments to prevent disease recurrence. These individuals often experience symptoms which are detrimental to their quality of life (QOL). Implementation of interventions for effective symptom management among these survivors is warranted. This review provides an overview of studies on the effectiveness of the previously developed interventions for breast cancer survivors undergoing adjuvant endocrine therapy on symptom alleviation and enhancement of QOL or health-related QOL (HRQOL). Five electronic databases were employed in the literature search. Study selection, data extraction and critical appraisal of the included studies were conducted by three authors independently. Twenty-four studies were included. Both pharmacological and non-pharmacological interventions are effective in addressing the symptoms associated with adjuvant endocrine therapy among the breast cancer survivors, and in improving their QOL, although discrepancies were noted between the studies in terms of the significance of these effects. Pharmacological and non-pharmacological interventions can be effective for symptom management among breast cancer survivors. Their implementation is recommended for effective survivorship care for these individuals. Further research on intervention development for breast cancer survivors is recommended to provide further evidence for the utility of the explored interventions in survivorship care for these patients.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".