657-P: Frequency Scanning Correlates Not Only with Glycemic Indices but Also with Fear of Hypoglycemia in Type 1 Diabetes Patients Using is-CGMS
Bibliographic record
Abstract
Frequency scanning with intermittently scanned CGMS (is-CGMS) is associated with glycemic indices. No data is available for its correlation with fear of hypoglycemia (FOH) , a well-known factor affecting quality of life and glycemic control in type 1 diabetes (T1DM) . The aim of the study was to analyze the association of scanning frequency with glycemic indices and FOH in T1DM patients using is-CGMS. T1DM patients using is-CGMS were eligible. Clinical data, Ambulatory Glucose Profile (AGP) reports were obtained from medical records. FOH was assessed using Hypoglycemia Fear Survey II (HFS-II) . We included 77 consecutive patients (58 females) , 38 were treated with insulin pump, 39 with multiple daily injections. Their mean age was 34.1+/-10.2 years and T1DM duration 14.7 +/-12.0 years. Mean glycemic indices were as follows: mean glucose - 155.8+/-29.8 mg/dl; GMI - 53.3+/-7.5 mmol/mol; TIR - 66.4+/-17.8%; TB70 - 4.5+/-4.1%; TB54 - 0.6+/-1.2%; TA180 - 29.2 +/-17.9%; TA250 - 9.6+/-10.4%; %CV - 36.7+/-8.3. The average scanning frequency was 13.8+/-7.8 scans/d. Mean HFS II scores were 16.1+/-7.2 and 18.7+/-12.2 in behavior and worry subscale, respectively. Correlation was found between scanning frequency and mean glucose, GMI, TIR, TB70, TA180, TA250, %CV and HFS-B (p<0.for all statistics) . In summary, for the first time, we report that higher scanning frequency is associated not only with better glycemic indices but also with less FOH in T1DM patients using is-CGMS. This constitutes a new argument for advising T1DM patients frequent scanning when using is-CGMS. Disclosure J.Hohendorff: Advisory Panel; Abbott. M.Malecki: Consultant; Abbott Diabetes, Boehringer Ingelheim International GmbH, Lilly Diabetes, Novo Nordisk, Speaker's Bureau; Ascensia Diabetes Care, AstraZeneca, Bayer AG, Merck & Co., Inc., Mundipharma, Servier Laboratories. P.W.Witek: Other Relationship; Abbott, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Medtronic, Merck & Co., Inc., Novo Nordisk, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH. M.Kania: None. M.Sudol: None. K.Hajduk: None. A.Stepien: None. K.Cyganek: Speaker's Bureau; Abbott Diabetes, Ascensia Diabetes Care, Bausch Health, Canada, Boehringer Ingelheim International GmbH, Lilly Diabetes, Medtronic, Novo Nordisk, Roche Diabetes Care. B.Kiec-wilk: n/a. T.Klupa: Advisory Panel; Abbott, BIOTON S.A., Sanofi, Research Support; Medtronic, Speaker's Bureau; Ascensia Diabetes Care, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".