Living as a Survivor: Sleep Disturbance, Fatigue, Depressive Mood, and Cognitive Dysfunction After Breast Cancer Treatment
Bibliographic record
Abstract
BACKGROUND: Many cancer survivors endure multiple symptoms while striving to return to a normal life. Those symptoms often co-occur and exacerbate one another; however, their interplay is not fully understood. OBJECTIVE: This study aimed to examine the occurrence and concurrence of sleep disturbance, fatigue, depressive mood, and cognitive dysfunction in posttreatment breast cancer survivors. METHODS: The data for this descriptive analysis were collected as part of the screening for a clinical trial. The occurrences of sleep disturbance, depressive mood, and cognitive dysfunction were each determined by the cutoff scores of the Pittsburgh Sleep Quality Index, Center for Epidemiological Studies Depression Scale, and Montreal Cognitive Assessment, respectively; fatigue was determined by meeting the International Classification of Diseases cancer-related fatigue criteria. RESULTS: A convenience sample of 81 women completed chemotherapy or/and radiation for stage I-III breast cancer an average of 23.1 (±SD = 9.0) months ago. Sleep disturbance (85%) was most prevalent, followed by fatigue (67%), depressive mood (46%), and cognitive dysfunction (29%). Of the survivors, 80% reported 2 or more co-occurring symptoms. Worsened subjective sleep quality, sleep disturbance, and daytime dysfunction significantly increased the risk of fatigue by 5.3, 4.3, and 4.3 times (all P < .001) and depression by 2.0, 2.7, and 3.0 times (all P < .05), respectively. CONCLUSION: Sleep disturbance significantly increased the risk of survivors' fatigue and/or depressive mood after cancer treatment completion. IMPLICATION FOR PRACTICE: Effectively managing sleep disturbance and improving the individual's sleep perception may subsequently reduce fatigue and/or depressive mood among breast cancer survivors. Nonpharmacological strategies for managing multiple posttreatment symptoms are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".