Patient-reported sleep disruption as an independent prognostic factor for overall survival in metastatic colorectal cancer.
Bibliographic record
Abstract
410 Background: Sleep disruption is a prevalent problem among cancer patients and survivors, but clinical correlates of poor sleep are understudied, especially in colorectal cancer. We recently showed that metastatic breast cancer patients with poor sleep efficiency have a shorter overall survival. The primary study objective is to clarify the relationship between subjective sleep disruption and survival in patients with metastatic colorectal cancer (MCC). Methods: 240 pts (63% male, mean age=59; SD=11.0) treated for MCC in 1st to 5th line of 5-fluorouracil based chemotherapy completed the QOL Questionnaire (EORTC QLQ-C30). We considered sleep to be disrupted if patients reported little to severe trouble sleeping (scores >0 ). Multivariate Cox models included age, gender, site of primary tumor, stage at diagnosis, number of metastatic sites, performance status and prior chemotherapy. Results: 65.4% of the patients reported mild to severe sleep disruption according to EORTC QLQ-C30. Patients with trouble sleeping had poorer overall survival as compared to those without sleep disruption (HR: 1.47 [1.11 to 1.95]; p=0.008). Respective median survival times (months) were 14.2 [95% CI: 12.3 to 16.1] and 17.7 [10.0 to 25.3]. The survival benefit observed in patients without sleep disruption remained statistically significant after adjustment for other prognostic factors. The final multivariate prognostic model included subjective sleep disruption (HR: 1.49 [1.11 to 2.00]; p=0.009), number of metastatic sites (p<0.001), performance status (p=0.023), and prior chemotherapy (p<0.001). Conclusions: Our findings show that patients reported sleep disruption is an independent prognostic factor for overall survival in MCC. Future research is needed to determine the mechanisms of sleep disruption and its effect on survival, and whether treatment of sleep disruption can improve survival in MCC.
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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 | 0.000 | 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".