135-LB: A Variable Impact of the Wave 1 and Wave 2 Lockdowns of the COVID-19 Pandemic on Glycemic Control—A Longitudinal Analysis of FGM Adult Users From Poland
Bibliographic record
Abstract
WHO declared COVID19 pandemic in March 2020. In Poland, the particularly severe restrictions related to COVID19 pandemic were implemented during Wave 1. All activities were prohibited except necessary everyday ones. From late May to July 2020 restrictions were gradually lifted. In October 2020, due to Wave 2 of COVID19 pandemic some restrictions were reimplemented, however, their impact on everyday life was smaller than during the Wave 1. The aim of the study was to analyze the impact of different phases of COVID19 pandemic on glycemic control in patients with diabetes using FGM. Longitudinal data of 469 adults aged 18-64 were analyzed. Analyzed time periods were every first 45 days of each quarter of the year 2020. Q1 stands for the first pre-pandemic quarter of 2020, Q2 correspondences to the severe lockdown of Wave 1, Q3 to time of most restrictions lifted, and Q4 to less strict lockdown of Wave 2. Mean glycemic indices are shown in Table 1. In summary, Wave 1 and Wave 2 of COVID19 pandemic had different impact on glycemic control in patients with diabetes. The severe lockdown of the first wave resulted in adults with diabetes in improvement of glycemic indices, particularly TIR. Common findings during both lockdowns were reduction in TBR and lower glycemic variability as compared to the preceding periods. Disclosure J. Hohendorff: Speaker's Bureau; Abbott, Novo Nordisk, Ascensia Diabetes Care, Dexcom, Inc., Bayer Inc. K. Kao: Employee; Abbott Diabetes. L. Brandner: Employee; Abbott Diabetes. M. Malecki: Advisory Panel; Abbott Diabetes. Speaker's Bureau; AstraZeneca, AlfaSigma. Advisory Panel; Novo Nordisk, Lilly, Sanofi. Speaker's Bureau; Merck & Co., Inc. Research Support; Medtronic. Advisory Panel; Dexcom, Inc. Speaker's Bureau; BIOTON S. A., Servier Laboratories, KRKA. Advisory Panel; Bayer Inc.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".