Overtreatment and undertreatment in a sample of elderly people with diabetes
Bibliographic record
Abstract
Aims In older adults with type 2 diabetes (T2D), overtreatment remains prevalent and undertreatment ignored. The main objective is to estimate the prevalence and examine factors associated with potential overtreatment and undertreatment Method Observational study conducted within an administrative database of older adults with T2D who registered in 2018 at the Portuguese Diabetes Association. Participants were categorized either as potentially overtreated (HbA1c≤7.5%), appropriately on target (HbA1c≥7.5–≤9%), or potentially undertreated (HbA1c>9%). Results of 444 participants, potential overtreatment, and undertreatment were found in 60.5% and 12.6% of the study population. Taking the patients on target as a comparator, the group of potentially overtreated showed to be more males (61.3% vs.52.2%), less-obese (34.1% vs.39.2), higher cardiovascular diseases (13.7% vs.11%), peripheral vascular diseases (16.7% vs.12.8%), diabetic foot (10% vs.4.5%), and severe kidney disease (5.2% vs.4.5%). Conversely, the potentially undertreated participants were more females (64.2% vs.47.7%), obese (49% vs.39.2%), had more dyslipidemia (69% vs.63.1%), peripheral vascular disease (14.2% vs.12.8%), diabetic foot (8.9% vs.4.5%), and infections (14.2% vs.11.9%). The odds of potential overtreatment were mostly decreased by 59% of females, 73.5% in those with retinopathy, and 86.3% in insulin, 65.4% sulfonylureas, and 66.8% in SGLT2 inhibitors users. Contrariwise, an increase in the odds of potential undertreatment was more than 4.8times higher in insulin, and more than 3.1times higher in sulfonylureas users. Conclusion potential overtreatment and undertreatment in older adults with T2D in routine clinical practice should guide the clinicians to balance the use of newer oral antidiabetic agents considering its safety profile regarding 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".