974-P: BGM Readings in Range as a Marker of Glycemic Variability in People with Type 2 Diabetes—Interim Analysis from an Ongoing Prospective Study
Bibliographic record
Abstract
Background: Glycemic variability (GV) is emerging as an independent risk factor for long-term complications. However, measuring GV using CGM is expensive and used sparingly in India. We evaluated BGM readings in-range, above range, and below range (RIR, RAR, RBR) using the OneTouch Verio Flex® (OTVF) BGM with the OneTouch Reveal® (OTR) application to assess GV and its correlation with TIR, TAR and TBR measured using CGM. Aims and Objectives:1. To evaluate structured BGM as a tool for estimating GV 2. To assess the correlation between BGM-measured RIR, RAR, RBR and the GV parameters reported on AGP using Libre Pro CGM Materials and Methods: We performed data analysis of 76 completed patients from an ongoing, prospective, multicentre, investigator-initiated study being conducted in New Delhi, India. Persons with T2D on stable treatment (OADs, OADs + Insulin, or Insulin) for ≥ 4 weeks were randomized 1:1 to perform daily 4-point profiles or 7-point profiles using structured BGM readings for 14 days while also wearing blinded CGM using the Libre Pro system. CGM parameters were compared with BGM readings in different ranges measured on the OTR platform. Results: Most of the PwD performed fewer BGM readings than advised (average readings/day 3.62±1.4). There were strong correlations between daily average glucose, TIR and RIR, TAR and RAR, TBR and RBR (r 0.926, p<0.0001; r 0.805, p<0.00001; r 0.826, p<0.00001; r 0.348, p=0.0092, respectively). Further, there was a significant correlation between 14 days average glucose (158.3mg/dl±56.85 vs 186.13mg/dl±58.9, p=0.0139), TIR vs RIR (66.09%±27.59 vs 52.477%±28.65), TAR vs RAR (30%±29.45 vs 46.95%±28.78, p=0.0029), and TBR vs RBR (3.91%±6.27 vs 0.58%±1.54, p=0.0002). Conclusion: Structured BGM using OneTouch Verio Flex® paired with the OneTouch Reveal application may be used to assess glycemic variability. Disclosure B.M.Makkar: Advisory Panel; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Novartis, Novo Nordisk, Sanofi, Research Support; Lifescan. K.Soota: Research Support; LIFESCAN. V.Gupta: None. J.K.Sharma: None. G.Khurana: None. R.Chawla: None. Funding LifeScan
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".