Secular Trends in the Clinical Characteristics of Type 2 Diabetic Patients With Severe Hypoglycemia Between 2008 and 2013
Bibliographic record
Abstract
BACKGROUND: We investigated the trends in the clinical characteristics and prescriptions of type 2 diabetic patients with severe hypoglycemia because the prescription rate of antidiabetic agents has significantly changed recently. METHODS: A total of 193 patients with type 2 diabetes with severe hypoglycemia induced by antidiabetic agents between 2008 and 2013 were divided into three groups based on the period of visit: 2008 - 2009, 2010 - 2011 and 2012 - 2013. RESULTS: While the proportion of patients with severe hypoglycemia using insulin (from 55% to 74%), biguanides (from 6% to 20%), glinides, and dipeptidyl peptidase-4 inhibitors significantly increased, those using sulfonylureas (from 45% to 20%) significantly decreased. Errors of drug use significantly increased as a trigger of hypoglycemia in recent years. The number of antidiabetic agents (from 1.9 ± 0.6 to 2.3 ± 0.7), non-diabetic agents (from 2.3 ± 2.4 to 4.3 ± 3.3), and total drugs prescribed were significantly higher in recent years among patients receiving insulin therapy. CONCLUSIONS: Polypharmacy especially in patients receiving insulin therapy and errors of drug use have increased in type 2 diabetic patients with severe hypoglycemia in recent years. Intensive education in the usage rule of drugs is considered to be important in order to prevent severe hypoglycemia.
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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 | 0.002 |
| 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".