Efficacy of a Novel Interim Intervention Technique (IIT) with Retrospective Flash Glucose Monitoring to Improve Glycemic Control
Bibliographic record
Abstract
Aim: Retrospective blinded flash glucose monitoring system (FGMS) is typically used for 14 days, prior to modification of therapy. We sought to assess the efficacy of a novel approach wherein an interim analysis was done within a week of starting FGMS and utilized to implement therapeutic modifications. The same sensor was reassessed within the following week to see the changes in glycemic control, thereby maximizing utility of a single sensor. Methods: This is a retrospective analysis of 1consecutive adults with T2DM and HbA1c >7% on pharmacotherapy (oral agents and/or insulin) at a single centre. Patients started on blinded FGMS (Freestyle Libre Pro) were assessed within 1 week to get baseline estimate of glycemic trends. Glucose target range was set at 70-180 mg/dL. Patients kept a food log while on FGMS. Based on the glucose profile reports, dietary and pharmacotherapy changes were made and patients were re-evaluated in the next 7 days to assess the changes due to IIT, while wearing the same sensor. Analysis of pre and post: daily average glucose (DAG), time in target range (TITR), time above target range (TATR) and time below target range (TBTR) was performed. Results: At baseline, patients had a DAG of 191.3 mg/dL. Average time for interim analysis was 5 days after FGMS initiation. After IIT, the DAG dropped to 137.4 mg/dL, within 14 days (p <0.001). The TATR dropped from 52.1% to 18.3% (p<0.001), with a concurrent decrease in TBTR from 5.7% to 1.5% (p<0.001). Recurrent hypoglycemias were detected in 27 (25%) patients, with average TBTR being 21.1% and TITR being 65.7% on interim analysis. Following IIT, the TBTR reduced to 1.9% (p<0.001) and TITR changed to 86.8% (p<0.001), without increase in TATR in these patients. Conclusion: Using IIT, changes to patient’s lifestyle and/or pharmacotherapy can be made within a few days of FGMS, with the ability to gauge subsequent changes while still utilizing the same sensor. Significant improvement in glycemic control was achieved using this technique. Disclosure A.B. Jain: Speaker's Bureau; Self; Abbott, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Sanofi.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".