Sleep Quality in Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: This study aims to evaluate the sleep quality in patients with type 2 diabetes mellitus (T2DM), and to assess the relevance of other factors to sleep quality. METHODS: A cross-sectional study was carried out at the National Center for Diabetes, Endocrinology and Genetics (NCDEG) in Amman, Jordan, during the period from October 1, 2015 to December 31, 2015. A total of 1,211 (540 male and 671 female) patients with T2DM were recruited. Data were collected using the Pittsburgh sleep quality index (PSQI) to assess the sleep quality with a cutoff point of PSQI ≥ 8. Participants' demographic background data were also recorded. Statistical analysis was conducted using SPSS version 22. RESULTS: , and mean duration of diabetes was 10.3 ± 7.38 years. The mean PSQI score was 10.2 ± 3.10. In the present study, poor sleep quality was reported in 81% of participants. Multivariate logistic regression analysis revealed that poor sleep quality was significantly associated with high HbA1c, female gender, smoking, unemployment, and insulin use. The study showed that subjective sleep quality and quantity, night sleep disturbance, and daytime dysfunction were risk factors for poor glycemic control. CONCLUSIONS: In our series, patients with T2DM (81%) have poor sleep quality. Females, smokers, unemployed individuals, insulin users and patients with uncontrolled diabetes seem to be significantly at higher risk of poor sleep quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".