Insulin Use in Elderly Adults: Risk of Hypoglycemia and Strategies for Care
Bibliographic record
Abstract
Hypoglycemia is a significant problem in elderly adults with diabetes mellitus. Elderly individuals with diabetes mellitus are at greater risk than younger adults for hypoglycemic events. Several factors contribute to this risk, including the high prevalence of comorbidities, polypharmacy, cognitive impairment, and concomitant use of agents that interfere with glucose metabolism. To minimize the risk of hypoglycemia and maximize the benefits of glycemic control, guidelines typically recommend individualizing glycosylated hemoglobin (HbA1c) targets based on life expectancy, functional status, and individual goals. Although many individuals with type 2 diabetes mellitus will ultimately require insulin therapy to achieve and maintain glycemic control, earlier insulin initiation in elderly individuals may be warranted, particularly in those with renal, cardiovascular, or hepatic concerns that could interfere with the use of oral agents. There are few data on the use of insulin-or other glucose-lowering agents-in elderly adults, but limited evidence suggests that the use of insulin, especially insulin analogs, may be appropriate in this population. Insulin analogs offer a better pharmacokinetic profile, greater convenience, and less variable glycemic control than human insulin. Because of the high prevalence of cognitive impairment and other geriatric syndromes in elderly adults, clinicians should perform a comprehensive assessment of patients' ability to administer and monitor insulin therapy and recognize and treat 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".