Deprescribing oral antidiabetics in elderly patients: Do electronic leaflets across the world address it?
Bibliographic record
Abstract
Diabetes caused 6.7 million deaths in 2021, equating to one death every five seconds, with its global financial burden projected to rise from $1.32 trillion in 2015 to $2.12 trillion by 2030. Severe hypoglycemia necessitates interventions like deprescribing, behavioral strategies, and technology for prevention. Deprescribing aims to reduce unnecessary medication use, enhance rational prescribing, prevent prescribing cascades, and improve health outcomes in elderly patients. Evaluating electronic leaflets can support deprescribing based on patient-centered care and shared decision-making. Objective: To analyze information on deprescribing in oral antidiabetic leaflets from national medicines regulatory authorities, focusing on elderly patients with type 2 diabetes. Methods: This documental study analyzed electronic leaflets of oral antidiabetics from the official websites of nine Medicines Regulatory Authorities: Australia, Brazil, Canada, New Zealand, Singapore, South Africa, UK, USA, and EU, covering drugs listed in the WHO's Essential Medicines List 2023. The analysis focused on the alignment of deprescribing information with the Ontario deprescribing algorithm for oral antidiabetics developed by the Bruyère Institute in Canada. Results: Out of 72 expected leaflets, 64 (88.9 %) were retrieved. Only 18 leaflets (28.1 %) explicitly discussed deprescribing oral antihyperglycemics. Hypoglycemia and drug interaction risks were addressed in 55 leaflets (85.9 %). Caution for use in patients over 65 was mentioned in 32 leaflets (50 %), and 23 leaflets (35.9 %) addressed the risks of tight glucose and HbA1c targets. Conclusion: Despite a high retrieval rate, 11.1 % of leaflets were missing, and those available contained inconsistent deprescribing information. There are significant disparities in guidance across regulatory authorities. Standardized, updated leaflets that address deprescribing in frail older patients could enhance prescribers' confidence and support shared decision-making.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".