Insomnia in breast cancer: Independent symptom or symptom cluster?
Bibliographic record
Abstract
OBJECTIVE: This study examined insomnia in the context of breast cancer, both as an independent symptom and as a component of a symptom cluster that includes depression, anxiety, fatigue, and pain. METHOD: Women with a history of breast cancer currently taking an aromatase inhibitor and who had completed cancer treatment at least one month prior to enrollment were included (n = 413). Participants completed validated measures of insomnia, fatigue, pain, depression, and anxiety. Factor analysis was utilized to examine the extent to which these symptoms could be represented by common latent factors. Insomnia severity was then separated into a symptom cluster component (I-SC) and an insomnia-unique (I-U) component. The associations between each insomnia component and demographic and clinical factors were examined in multivariate models. RESULTS: A single-factor solution provided the best fit to the symptom measures. Some 53.3% of the variance in insomnia severity was captured by this symptom cluster (I-SC), with the remaining 43.7% being unique to insomnia (I-U). Unique patterns of demographic factors (e.g., age and body-mass index), but not clinical factors, were associated with each insomnia measure. SIGNIFICANCE OF RESULTS: Approximately 50% of insomnia severity was related to the symptom cluster, with the rest being unique to insomnia. Different sociodemographic risk factors were related to the different insomnia measures. Stronger underlying foundations for the mechanisms of each component could lead to refined diagnoses and targeted interventions for addressing the overall insomnia burden in cancer 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".