Natural Course of Insomnia Comorbid With Cancer: An 18-Month Longitudinal Study
Bibliographic record
Abstract
PURPOSE: This study aimed to assess the prevalence and natural course (incidence, persistence, remission, and relapse) of insomnia comorbid with cancer during an 18-month period. PATIENTS AND METHODS: All patients scheduled to receive a curative surgery for a first diagnosis of nonmetastatic cancer were approached on the day of their preoperative visit to participate in the study. A total of 962 patients with cancer (mixed sites) completed an insomnia diagnostic interview at the perioperative phase (T1), as well as at 2 (T2), 6 (T3) 10 (T4), 14 (T5), and 18 (T6) months after surgery. RESULTS: Findings revealed high rates of insomnia at baseline (59%), including 28% with an insomnia syndrome. The prevalence of insomnia generally declined over time but remained pervasive even at the end of the 18-month period (36%). Rates were greater in patients with breast (42% to 69%) and gynecologic (33% to 68%) cancer and lower in men with prostate cancer (25% to 39%) throughout the study. Nearly 15% of patients had a first incidence of insomnia during the study, and 19.5% experienced relapse. The evolution of symptoms varied according to sleep status. Remissions (patients becoming good sleepers) were much less likely for patients with an insomnia syndrome (10.8% to 14.9%) than for those with insomnia symptoms (42.0% to 51.3%). Most frequently (37.6%), patients with an insomnia syndrome at baseline kept that status throughout the 18-month period. CONCLUSION: Insomnia is a frequent and enduring problem in patients with cancer, particularly at the syndrome level. Early intervention strategies, such as cognitive-behavioral therapy, could prevent the problem from becoming more severe and chronic.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".