Prevalence, Clinical Characteristics, and Risk Factors for Insomnia in the Context of Breast Cancer
Bibliographic record
Abstract
STUDY OBJECTIVES: To estimate the prevalence of insomnia, describe clinical characteristics of sleep difficulties, assess the influence of cancer on the insomnia course, and identify potential risk factors involved in the development of insomnia among women who had received radiotherapy for non metastatic breast cancer. DESIGN: A sample of 300 consecutive women who had been treated with radiotherapy for non metastatic breast cancer first completed an insomnia screening questionnaire. Participants who reported sleep difficulties were subsequently interviewed over the phone to evaluate further the nature, severity, duration, and course of their insomnia. SETTING: N/A. PATIENTS OR PARTICIPANTS: N/A. INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: Nineteen percent (n=56) of the participants met diagnostic criteria for an insomnia syndrome. In most cases (95%), insomnia was chronic. The onset of insomnia followed the breast cancer diagnosis in 33% of the patients and 58% of the patients reported that cancer either caused or aggravated their sleep difficulties. Factors associated with an increased risk for insomnia were sick leave, unemployment, widowhood, lumpectomy, chemotherapy, and a less severe cancer stage at diagnosis. Among women with insomnia symptoms, the risk to meet diagnostic criteria for an insomnia syndrome was higher in those who were separated and had a university degree. CONCLUSIONS: Insomnia is a prevalent and often chronic problem in breast cancer patients. Although it is not always a direct consequence of cancer, pre-existing sleep difficulties are often aggravated by cancer. It is therefore important to better screen breast cancer patients with insomnia and offer them an appropriate treatment.
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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 | 0.003 |
| 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 | 0.000 | 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 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".