Physician experiences with sodium-glucose cotransporter (SGLT2) inhibitors, a new class of medications in type 2 diabetes, and adverse effects
Bibliographic record
Abstract
AIM: The primary aim of our study is to identify physicians who have witnessed a complication attributed to sodium-glucose cotransporter (SGLT2) inhibitors. The secondary aim is to determine the type, severity, and setting of the event (inpatient versus outpatient). BACKGROUND: Diabetes is an increasing public health burden with 9.9% of Canadians expected to be diagnosed with it in 2020. A prominent change with respect to treatment options since the publication of the revised Diabetes Canada guidelines in May 2016 concerned the SGLT2 inhibitors. Their favorable clinical profile has increased interest among clinicians, but there is still reason for caution. Because these drugs are new, the balance of benefits versus risks is not well understood. METHODS: We conducted a cross-sectional survey of all in-practice physicians (excluding pediatricians). Data were collected through an online survey. FINDINGS: Our survey identified 154 physicians who have identified one or more adverse drug reactions (ADRs) related to SGLT2 inhibitor use. A total of 173 ADRs were identified. In total, 20.6% of family physician respondents had witnessed one or more ADRs. The most common complication is mycotic infection (82 cases) with 47% identified as a low level of severity and occurring mostly in the outpatient setting. The second most common complication is diabetic ketoacidosis (43 cases) with 67% identified as a high level of severity and occurring mostly in the inpatient setting. Other identified complications include hyperkalemia (6 cases), renal insufficiency (15 cases), and even amputation (2 cases). Our survey is the first to document real-world complications from SGLT2 inhibitors. In the outpatient setting, mycotic infections are most common and most often benign. In the inpatient setting, diabetic ketoacidosis is the most common and is severe. This is an important take-home message for family physicians to tailor their practice and vigilance according to the practice setting.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".