Preventing Diabetes in Primary Care: A Feasibility Cluster Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: To determine the feasibility of implementing a large-scale primary care-based diabetes prevention trial. METHODS: A feasibility cluster randomized controlled trial was conducted in British Columbia, Canada, amongst adults with prediabetes using the Facilitated Lifestyle Intervention Prescription (FLIP) vs. usual care. FLIP included lifestyle advice, a pedometer, and telephone support from a lifestyle facilitator for 6 months. Indicators of feasibility included recruitment rates of family practices, participants and facilitators, as well as feasibility and retention rates in the FLIP program and study protocols. RESULTS: Six family practices participated; 59 patients were enrolled between October 2012 and March 2013. The trial protocol was acceptable to practices and participants and had a 95% participant retention rate over the 6 months (56/59). Adherence to the intervention was high (97%), with 34 of 35 patients continuing to receive telephone calls from the facilitator for 6 months. The mean cost of the intervention was C$144 per person. Compared with control, intervention participants significantly reduced weight by 3.2 kg (95% CI, 1.7 to 4.6); body mass index by 1.2 (95% CI, 0.7 to 1.7) and waist circumference by 3 cm (95% CI, 0.3 to 5.7). CONCLUSIONS: It is feasible to implement FLIP and to conduct a trial to assess effectiveness. A larger trial with longer follow up to assess progression to diabetes is warranted.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".