Use of basal insulin in the management of adults with type 2 diabetes: An Asia‐Pacific evidence‐based clinical practice guideline
Bibliographic record
Abstract
The objective of this study was to provide recommendations regarding effectiveness, safety, optimal starting dose, optimal maintenance dose range, and target fasting plasma glucose of five basal insulins (glargine U-300, degludec U-100, glargine U-100, detemir, and insulin protamine Hagedorn) in insulin-naïve adult patients with type 2 diabetes in the Asia-Pacific region. Based on evidence from a systematic review, we developed an Asia-Pacific clinical practice guideline through comprehensive internal review and external review processes. We set up and used clinical thresholds of trivial, small, moderate, and large effects for different critical and important outcomes in the overall certainty of evidence assessment and balancing the magnitude of intervention effects when making recommendations, following GRADE methods (Grading of Recommendations, Assessment, Development, and Evaluation). The AGREE (Appraisal of Guidelines, Research and Evaluation) and RIGHT (Reporting Items for practice Guidelines in HealThcare) guideline reporting checklists were complied with. After the second-round vote by the working group members, all the recommendations and qualifying statements reached over 75% agreement rates. Among 44 contacted external reviewers, we received 33 clinicians' and one patient's comments. The overall response rate was 77%. To solve the four research questions, we made two strong recommendations, six conditional recommendations, and two qualifying statements. Although the intended users of this guideline focused on clinicians in the Asia-Pacific region, the eligible evidence was based on recent English publications. We believe that the recommendations and the clinical thresholds set up in the guideline can be references for clinicians who take care of patients with type 2 diabetes worldwide.【提要】 为亚太地区初次使用胰岛素的成人2型糖尿病患者提供关于5种基础胰岛素(甘精胰岛素U-300、德谷胰岛素U-100、甘精胰岛素U-100型、地特胰岛素和中性鱼精蛋白胰岛素)的有效性、安全性、最佳起始剂量、最佳维持剂量范围和目标空腹血浆葡萄糖的推荐。基于系统回顾的证据,本综述通过全面的内部审核和外部审核流程制定了亚太地区临床实践指南。本综述在整体证据确定性评估中为不同重要和关键结局设置了微小、小、中等和大效应的临床阈值,并遵循GRADE(评估、发展、评价、建议的分级)方法来平衡干预效果的大小以进行推荐。本综述遵守了AGREE(指南评估、研究和评估)和RIGHT(卫生保健实践指南报告项目)指南报告检查清单。在工作组成员进行第二轮投票后,所有推荐和限定性陈述的一致率均达到了75%以上。在联系的44名外部评审人员中,本研究收到了33名临床医生和1名患者的意见。总体应答率为77%。为解决4个研究问题,本研究提出了2个强烈推荐、6个有条件推荐和2个限定性陈述。尽管本指南的预期用户主要是亚太地区的临床医生,但所选的证据基于最近的英文出版物。本综述提示,指南中制定的推荐和临床阈值可以成为全球关注2型糖尿病患者的临床医生的参考。.
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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 | 0.055 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".