Effect of Computer-Generated Tailored Feedback on Glycemic Control in People With Diabetes in the Community
Bibliographic record
Abstract
OBJECTIVE: It is unknown whether computer-generated, patient-tailored feedback leads to improvements in glycemic control in people with type 2 diabetes. RESEARCH DESIGN AND METHODS: We recruited people with type 2 diabetes aged ≥ 40 years with a glycated hemoglobin (A1C) ≥ 7%, living in Hamilton, Canada, who were enrolled in a community-based program (Diabetes Hamilton) that provided regular evidence-based information and listings of community resources designed to facilitate diabetes self-management. After completing a questionnaire, participants were randomly allocated to either receive or not receive periodic computer-generated, evidence-based feedback on the basis of their questionnaire responses and designed to facilitate improved glycemic control and diabetes self-management. The primary outcome was a change in A1C after 1 year. RESULTS: A total of 465 participants (50% women, mean age 62 years, and mean A1C 7.83%) were randomly assigned, and 12-month A1C values were available in 96% of all participants, at which time the A1C level had decreased by an absolute amount of 0.24 and 0.15% in the intervention and control groups, respectively. The difference in A1C reduction for the intervention versus control group was 0.09% (95% CI -0.08 to 0.26; P = 0.3). No between-group differences in measures of quality of life, diabetes self-management behaviors, or clinical outcomes were observed. CONCLUSIONS: Providing computer-generated tailored feedback to registrants of a generic, community-based program that supports diabetes self-management does not lead to lower A1C levels or a better quality of life than participation in the community-based program (augmented by periodic A1C testing) alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".