Randomized Trial of Long-Acting Insulin Glargine Titration Web Tool (LTHome) Versus Enhanced Usual Therapy of Glargine Titration (INNOVATE Trial)
Bibliographic record
Abstract
BACKGROUND: Basal insulin titration in the real world is often unsuccessful. LTHome, a web tool, applies a rules engine-based algorithm providing insulin titration advice directly to the patient. METHODS: This pilot, randomized trial evaluates basal insulin glargine titration by LTHome compared to enhanced usual therapy ([EUT]-diabetes education program) over 12 weeks. Important inclusion criteria: 18-75 years, type 2 diabetes, computer literacy, and HbA1c >7.0%. Trial protocol was approved by ethics board. RESULTS: We randomized 139 subjects. The achievement of primary composite outcome (four out of seven fasting plasma glucose [FPG] within 5-7.2 mmol/L + mean for three consecutive FPG within 5-7.2 mmol/L + no severe hypoglycemia) was 15% in LTHome versus 41% in EUT (noninferiority not met, P-value = 0.92). Other outcomes were similar between the LTHome and EUT arms: alternate composite outcome achievement (last five FPG mean within the range of 5-7.2 mmol/L + no hypoglycemia, 47% and 51%, P = 0.73); A1c reduction (-1.0% and -1.1%, P = 0.66); proportion achieving A1c ≤7% (14% and 20%, P = 0.36); and hypoglycemia incidence (31% and 37%, P = 0.4), respectively. Patient satisfaction score improvements were greater in LTHome versus EUT (change in fear of hypoglycemia score P = 0.04 and change in diabetes distress score P = 0.04). The mean number of additional healthcare provider visits was 0.13 for LTHome and 1.22 for EUT (P < 0.01). CONCLUSION: INNOVATE trial suggests clinical utility of LTHome compared to EUT in real-life settings. Further research is needed to evaluate the efficacy and safety of automated insulin titration algorithms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".