Insomnia and self-reported infections in cancer patients: An 18-month longitudinal study.
Bibliographic record
Abstract
OBJECTIVE: This study, conducted in cancer patients, aimed to evaluate longitudinally whether the presence of insomnia is associated with the occurrence of self-reported infections. METHOD: Patients scheduled to receive a curative surgery for a first diagnosis of nonmetastatic cancer were solicited on the day of their preoperative visit. In total, 962 cancer patients completed the Insomnia Interview Schedule and a clinical interview to assess infectious symptoms at 6 time points: at the perioperative phase (baseline), as well as 2, 6, 10, 14, and 18 months later. At each assessment, patients were categorized into the following 3 groups: insomnia syndrome (SYN), insomnia symptoms (SX), and good sleepers (GS). RESULTS: The analyses revealed that SYN patients at 1 time point were at a significantly higher risk of reporting at least 1 infectious episode at the subsequent assessment (OR = 1.31, p = .04), whereas SX patients were at a marginally significant higher risk of reporting such episodes (OR = 1.19, p = .08), as compared with GS. CONCLUSIONS: Although these results need replication and the causality needs to be established, they suggest that insomnia may potentiate the risk of experiencing infections during the cancer care trajectory. (PsycINFO Database Record
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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) | 0.000 | 0.001 |
| 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".