Patient Factors Associated with Increased Cancer Worry, Fatigue, and Impact on Work Following a Breast Cancer Diagnosis: A Cross-Sectional Analysis
Bibliographic record
Abstract
Introduction: A breast cancer diagnosis may result in disabling effects which may persist after treatment. The aim of this study was to identify patient factors that are associated with increased cancer worry, fatigue, and impact on work. Methods: Women with a history of breast cancer, aged ≥18 years, and English-speaking were recruited through the Love Research Army between October and November 2019. Participants completed demographic and clinical questions alongside the BREAST-Q Cancer Worry, Fatigue, and Impact on Work scales. Univariable and multivariable regression analyses were used to identify participant characteristics associated with each scale. Results: Cancer Worry, Fatigue, and Impact on Work scales were completed by n = 1680, n = 1037, and n = 873 participants, respectively. Most participants were older than 50 ( n = 1,470, 87.5%), married ( n = 1229, 73.2%), white ( n = 1557, 92.7%), and had undergone surgery for cancer treatment ( n = 1,472, 87.6%). Increased Cancer Worry was significantly associated ( P < .04) with younger age, less time since diagnosis, pain related to cancer/treatment, recurrence, prior chemotherapy, and ongoing breast edema. Increased Fatigue was significantly associated ( P < .01) with elevated BMI, less time since diagnosis, ethnicity, employment status, recurrence, prior chemotherapy, ongoing pain, and difficulty sleeping secondary to treatment. Decreased Impact on Work scores was significantly associated ( P < .04) with chemotherapy administration, shorter time since diagnosis, employment, fatigue related to treatment, breast edema, and ongoing pain. Conclusion: This study reveals patient characteristics associated with increased cancer worry, fatigue, and a negative impact on work following a breast cancer diagnosis. These findings can inform clinical and research initiatives to better support patients through treatment and survivorship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".