When Insulin Therapy Fails: The Impact of SGLT2 Inhibitors in Patients With Type 2 Diabetes
Bibliographic record
Abstract
Insulin is the most effective therapy for achieving optimal glycemic control; however, many patients with type 2 diabetes on an intensified treatment regimen fail to achieve the recommended HbA1c target (1,2) and face the risk of adverse effects such as hypoglycemia and weight gain (3). The addition of sodium–glucose cotransporter 2 (SGLT2) inhibitors to a regimen of insulin therapy in this patient population has the potential to mitigate insulin-related weight gain and risk of hypoglycemia, with the added benefit of insulin dose reduction (4). Randomized controlled trials (RCTs) have shown improved clinical outcomes of SGLT2 inhibitors as monotherapy and as an add-on to oral and insulin therapy, but there is a paucity of real-world (RW) studies evaluating similar outcomes. Data extracted from WebDR (5) was used to evaluate the RW clinical impact of SGLT2 inhibitors (initiation of canagliflozin or dapagliflozin between February 2014 and December 2016) as an add-on to insulin therapy in patients with type 2 diabetes not achieving glycemic targets (those with HbA1c >7% [>53 mmol/mol]). Empagliflozin was excluded because of inadequate sample size. Ethical approval …
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| 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".