The impact of having a sister diagnosed with breast cancer on cancer‐related distress and breast cancer risk perception
Bibliographic record
Abstract
BACKGROUND: A family history of breast cancer has been shown to affect psychosocial functioning. However, the majority of research has focused on the daughters of patients with breast cancer and families with multiple relatives with the disease. The purpose of the current study was to examine cancer-related distress and breast cancer risk perception, and further examine the predictors of these outcomes, in the sisters of newly diagnosed patients with breast cancer without a previous family history of the disease. METHODS: Sisters of newly diagnosed index breast cancer patients were identified and asked to complete a study-specific questionnaire (demographics and cancer risk perception) and the Impact of Events Scale. Pathological information was abstracted from the medical chart for the index breast cancer patients. RESULTS: A total of 205 sisters completed the questionnaires. The mean time between breast cancer diagnosis and the sisters' completion of the questionnaire was 9.8 months. Approximately one-half of the women scored in the moderate or severe distress range. The most significant predictor of cancer-related distress was perceived lifetime breast cancer risk (P = .04). Women with a lifetime risk of breast cancer > 20% were more than twice as likely to have moderate or severe distress compared with those with a lifetime risk of < 20%. CONCLUSIONS: Cancer-related distress is high in the sisters of newly diagnosed patients with breast cancer in whom there is no other family history of breast cancer. Specifically, women with a perceived lifetime risk of breast cancer of > 20% experienced the highest levels of distress. Future interventions that target this group should be considered.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".