Risk of Obstructive Sleep Apnea Assessment Among Patients With Type 2 Diabetes in Taif, Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is a common condition in middle-aged persons worldwide. The major factor risk of this disease is obesity. METHODS: A cross-sectional study was performed at King Abdul-Aziz Specialist Hospital. A STOP-BANG questionnaire formed of eight questions was used to assess the risk of OSA among type 2 diabetic patients. The scoring scale is categorized into three groups: low (0 - 2), intermediate (3 - 4) and high (5 - 8), respectively. By this study, we aimed to assess the risk of OSA among diabetes patients in Taif city. RESULTS: Of the patients, 57.9% had mild risk, 26.9% had moderate risk and 15.2% had severe risk for OSA. There was a moderate positive relationship between age and STOP-BANG score. There was no significant correlation between the score and last fasting blood sugar and HbA1c's level, with P values of 0.554 and 0.335, respectively. There was a significant relationship between the type of treatment and the risk of developing OSA (P < 0.001). Percentage of patients with severe risk was significantly higher in those taking both insulin and oral drugs than those taking insulin alone or oral drugs alone. CONCLUSIONS: The OSA risk and prevalence is much higher in diabetics than in general population, with the risk increasing with age. The risk is higher in diabetic patients who are receiving both oral hypoglycemic drugs and insulin. The screening of OSA among diabetic patients is necessary to identify those at severe risk and manage this problem, which may remain undiagnosed in many patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".