An audit and feedback‐based intervention to improve diabetes management in the year after transfer to adult type 1 diabetes care: A multi‐center quasi‐experimental study
Bibliographic record
Abstract
AIM: To test whether an audit and feedback-based intervention improved HbA1c 12 months after transfer to type 1 diabetes adult care. METHODS: Multi-centre, quasi-experimental pre-post study of an AF-based intervention targeting paediatric diabetes teams, which encouraged the implementation of an evidence-informed structured transition process at five paediatric diabetes centres in Ontario, Canada. Participants entered the study at their final paediatric visit. A parallel control cohort was ascertained using population-based administrative datasets. The primary outcome was HbA1c 12 months after transfer. The main exposure was the study period: pre-implementation (June 2018-May 2019); early-implementation (June 2019-September 2020); and late-implementation (October 2020-September 2021). Multivariable linear regression models were fit separately in each cohort. RESULTS: There were 449 and 2844 individuals in the intervention and control cohorts, respectively. Twelve months after transfer, participants in the late-implementation intervention cohort had an HbA1c that was, on average, 0.41% lower than participants in the pre-implementation period (p = 0.016). Among the control cohort, there was no significant difference in the HbA1c 12 months after transfer between study periods. CONCLUSIONS: We found an effect of the intervention on glycaemic management one year following transfer to adult care. Future work will focus on refining and testing the effectiveness of the intervention in an expanded number of study sites and in collaboration with adult diabetes care providers.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".