Factors associated with improved glycemic control following continuous subcutaneous insulin infusion therapy in patients with type 2 diabetes uncontrolled with bolus‐basal insulin regimens: <scp>A</scp> n analysis from the <scp>OpT2mise</scp> randomized trial
Bibliographic record
Abstract
This analysis investigated factors associated with the decrease in HbA1c in patients receiving continuous subcutaneous insulin infusion (CSII) in the OpT2mise randomized trial. In this study, patients with type 2 diabetes and HbA1c >8% following multiple daily injections (MDI) optimization were randomized to receive CSII (n = 168) or MDI (n = 163) for 6 months. Patient-related and treatment-related factors associated with decreased HbA1c in the CSII arm were identified by univariate and multivariate analyses. CSII produced a significantly greater reduction in HbA1c than MDI, and the treatment difference increased with baseline HbA1c. In the CSII arm, the only factors significantly associated with decreased HbA1c were higher baseline HbA1c (P < .001), geographical region (P < .001), higher educational level (P = .012), higher total cholesterol level (P = .002), lower variability of baseline glucose values on continuous glucose monitoring (P < .001) and the decrease in average fasting self-monitored blood glucose at 6 months (P < .001). These findings suggest that CSII offers an option to improve glycemic control in a broad range of patients with type 2 diabetes in whom control cannot be achieved with MDI. OpT2mise ClinicalTrials.gov number: NCT01182493 (https://clinicaltrials.gov/).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".