Qualidade do sono em pacientes insulinodependentes
Bibliographic record
Abstract
Introduction: Diabetes Mellitus is a chronic disease that may be of acquired cause or congenital, which is characterized by an inability or deficiency of the body to produce insulin in sufficient quantities. Sleep disorders with type 2 diabetes constitutes risk factors that can aggravate the pathology. This happens through the insulin resistance witch interferes with the metabolic control. During sleep, in health individuals, there is a balance between insulin secretion and glucose, however in diabetics, the balance is compromised by the occurrence of hypoglycemia. Objective: To assess the impact of sleep loss on therisk of worsening diabetesin insulin-dependent patientsand to addresstherelationship between sleep quality as an influencing factor in glucose control. Materials and Methods: This study is a cross-sectional observational study and was carried out from September 2016 to June 2018. The population of this study is made up of 200 individuals and the sample was collected at Guarda Hospital and Trancoso Health Care, whose average ages is 64,80 ± 13,670 years. Results: 31% of the individuals use medication to sleep better. The mean daily insulin dose is 47,14 IU. The female gender had a higher incidence of sleep disturbances (47,5%), a high risk of OSAS (47,5%) and a higher percentage of restless legs (16,8%) compared to the male gender. The daily amount of insulin isrelated to BMI (p=0,025)and therisk of developing DM. Discussion/Conclusion: Given theresults obtained, we can conclude that both the daily amount of insulin and BMI have an important role in sleep quality and quality of life in type 2 diabetics.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".