Preparing to Meet the Needs of a Growing Older Adult Population with Type 1 Diabetes: A Narrative Review
Bibliographic record
Abstract
The prevalence of diabetes is rising among older adults. While most diabetes cases in older adults are type 2 diabetes mellitus (T2D), advances in type 1 diabetes mellitus (T1D) management and rising rates of adult-onset T1D have translated into a growing number of individuals with T1D living into older adulthood. This narrative review integrates existing evidence on management of T1D in older adults with expert opinions to provide practical guidance for generalists increasingly encountering the unique challenges and complexities of this growing population. The profound heterogeneity in clinical presentation, pathobiology, and disease progression across the older adult population can make it challenging to differentiate older adults with T1D from those with insulin treated T2D, particularly in adult-onset cases. However, timely diagnosis is critical to minimize exposure to hyperglycemia and reduce the risk for complications, as individuals with T1D rely entirely on exogenous insulin and require intensive self-management to prevent acute complications like hypoglycemia and ketoacidosis. Self-management of T1D in older adults presents unique challenges, including a high risk of hypoglycemia that must be mitigated in the setting of a lifelong requirement for insulin and evolving mismatches between intensive self-management demands and an older person's capacity for self-care. The growing number of older adults with T1D underscores a lack of access to specialized care and limited training and resources for evidence-based management in primary care and post-acute/long-term care settings, as well as the dearth of high-quality clinical evidence specific to this population to inform care. Research to support changes across healthcare systems and at the policy level, in combination with education and multi-specialty collaboration, will ensure that healthcare providers and health systems are equipped and prepared to better meet the needs of the growing population of older adults with T1D.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".