Promoting and supporting children’s health and healthcare during COVID-19 – International Paediatric Association Position Statement
Bibliographic record
Abstract
Introduction We investigated trends in the proportion of diabetes treatment and glycemic control, which may be altered by recent advances in insulin and non-insulin drugs, in Japanese patients with type 2 diabetes. Research design and methods A serial cross-sectional study was performed using a multicenter large-population database from the Japan Diabetes Clinical Data Management study group. Patients with type 2 diabetes who attended clinics belonging to the study group between 2002 and 2018 were included to examine trends in glycated hemoglobin A1c (HbA1c) by treatment group using multivariable non-linear regression model. Results The proportion of patients with insulin only decreased from 15.0% to 3.6%, patients with insulin+non-insulin drugs increased from 8.1% to 15.1%, patients with non-insulin drugs increased from 50.8% to 67.0%, and those with no drugs decreased from 26.1% to 14.4% from 2002 to 2018, respectively. The HbA1c levels of each group, except for no drugs, continued to decrease until 2014 (unadjusted mean HbA1c (%) from 2002 to 2014: from 7.89 to 7.45 for insulin only, from 8.09 to 7.63 for insulin+non-insulin, and from 7.51 to 6.98 for non-insulin) and remained unchanged thereafter. Among insulin-treated patients, use of human insulin decreased, use of long-acting analog insulin increased, and concomitant use of non-insulin drugs increased (from 35.1% in 2002 to 80.9% in 2018), which included increased use of dipeptidyl peptidase 4 inhibitors, sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide 1 receptor agonists, and the persistently high use of metformin. Conclusions During the past two decades, combined use of insulin and non-insulin drugs increased and glycemic control improved and leveled off after 2014 in Japanese patients with type 2 diabetes. Further studies of the trend in association with age and factors related to metabolic syndrome are necessary to investigate strategies aiming at personalized medicine in diabetes care.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".