0826 A Comparison of Pre-Treatment Sleep and Symptom Profiles in Age-Matched Breast and Prostate Cancer Patients
Bibliographic record
Abstract
Breast and prostate cancer patients report sleep disturbance and other problematic symptoms even before beginning cancer treatment. The current analysis examined whether pre-treatment prostate and breast cancer patients differ on sleep quality, insomnia symptom severity, fatigue, and anxiety and depression symptoms. Patients newly diagnosed with non-metastatic breast and prostate cancer were recruited from the Dr. H. Bliss Murphy Cancer Centre in St. John’s, Newfoundland. Participants completed the Consensus Sleep Diary for one week, the Pittsburgh Sleep Quality Index (PSQI), the Insomnia Severity Index (ISI), the Multidimensional Fatigue Symptom Inventory - Short From (MFSI-SF) and the Hospital Anxiety and Depression Scale (HADS). A MANOVA was conducted to examine sex differences on the sleep diary variables, PSQI, ISI, MFSI-SF, and HADS. Participants were 28 males and 28 females (N = 56) with a mean age of 68. Using the recommended cut-off of 5 on the PSQI, 82% of females and 64% of males had poor sleep quality. Twice as many females (21% vs. 11%) could be classified as having moderate to severe insomnia on the ISI (a score of 15+). Females were more likely than males to experience clinically significant anxiety (17% vs. 7%) and depression symptoms (11% vs. 4%). Females reported longer sleep latency (35.68 minutes vs. 16.68 minutes; F(1, 42) = 9.860, p = .003), less total sleep time (6.61 hours vs. 7.76 hours; F(1, 42) = 7.893, p = .008), and worse sleep efficiency (78% vs. 86%; F(1, 42) = 5.683, p = .022), compared to males. Males and females did not differ significantly on global scores of the PSQI, ISI, or MFSI-SF. Results of the current study suggest that breast cancer patients are entering treatment with poorer sleep and mood compared to prostate cancer patients. These pre-treatment differences may make women more vulnerable to poorer functioning during and after completing cancer treatment. Dr. Garland is supported by a New Investigator Award and seed funding from the Beatrice Hunter Cancer Research Institute (BHCRI).
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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 | 0.000 | 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) | 0.000 | 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".