Adoption of sodium‐glucose cotransporter‐2 inhibitors among prescribers caring for nursing home residents
Bibliographic record
Abstract
BACKGROUND: Sodium-glucose cotransporter-2 inhibitor (SGLT2I) use has increased among community-dwelling populations, but little is known about how clinicians have prescribed them for US nursing home (NH) residents. We described the adoption of SGLT2Is by prescribers caring for long-stay NH residents by clinician specialty and over time, compared with sulfonylureas, an older diabetes medication class. METHODS: We conducted a retrospective cohort study of prescribers of SGLT2Is and sulfonylureas for all long-stay US NH residents aged 65 years or older (2017-2019). Using 100% of Medicare Part D claims linked to prescriber characteristics data, we identified all dispensings of SGLT2Is and sulfonylureas for long-stay NH residents and their associated prescribers. We described the distribution of prescriber specialties for each drug class over time as well as the number of NH residents prescribed SGLT2s versus sulfonylureas. We estimated the proportions of prescribers who prescribed both drug classes versus only sulfonylureas or only SGLT2Is. RESULTS: We identified 36,427 unique prescribers (SGLT2I: N = 5811; sulfonylureas: N = 35,443) for 117,667 NH residents between 2017 and 2019. For both classes, family medicine and internal medicine physicians accounted for most prescriptions (75%-81%). Most clinicians (87%) prescribed only sulfonylureas, 2% prescribed SGLT2Is only, and 11% prescribed both. Geriatricians were least likely to prescribe only SGLT2Is. We observed an increase in the number of residents with SGLT2I use from n = 2344 in 2017 to n = 5748 in 2019. CONCLUSIONS: Among NH residents, most clinicians have not incorporated SGLT2Is into their prescribing for diabetes, but the extent of use is increasing. Family medicine and internal medicine physicians prescribed the majority of diabetes medications for NH residents, and geriatricians were the least likely to prescribe only SGLT2Is. Future research should explore provider concerns regarding SGLT2I prescribing, particularly adverse events.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".