Risk of Developing Sudden Sensorineural Hearing Loss in Diabetic Patients
Bibliographic record
Abstract
OBJECTIVES: To explore the risk of development of sudden sensorineural hearing loss (SSHL) among diabetes mellitus patients. STUDY DESIGN: A retrospective cohort study. SETTING: Population-based study of Taiwan National Health Insurance Research Database. PATIENTS: Approximately 26,556 newly diagnosed diabetic patients and 26,556 comparison subjects without diabetes mellitus were selected from claims made during 2000 to 2004. MAIN OUTCOME MEASURES: The incidence of sudden sensorineural hearing loss (SSHL) at the end of 2009 was determined. RESULTS: The incidence of SSHL was 1.54-fold higher in the diabetic group compared with the nondiabetic group (1.29 versus 0.78 per 1,000 person-years), and using Cox proportional hazard regressions, the adjusted hazard ratio (HR) was 1.592 (95% confidence interval [CI], 1.295-1.957). The risk of developing SSHL increased substantially to an HR of 2.060 (95% CI, 1.051-4.037), for patients who required triple antidiabetic medication compared with diabetic patients treated with a single antidiabetic drug. An increased risk of developing SSHL, with adjusted HRs of 1.574 (95% CI, 1.091-2.271) and 2.842 (95% CI, 1.880-4.295), was observed in diabetic patients with newly diagnosed comorbidities of retinopathy or retinopathy combined with renal insufficiency, which are both expected to reflect the severity of diabetes. CONCLUSION: Diabetes mellitus was significantly associated with an increased risk of developing SSHL. Coronary heart disease or retinopathy comorbidities in diabetic patients seemed to be associated with an increased risk of developing SSHL. The risk of developing SSHL increased with the severity of diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".