Standardized disease-related measures in diabetes research: results from a global consensus process.
Bibliographic record
Abstract
Background: A lack of disease-related consensus measures for type 2 diabetes interventions is a barrier to comparing interventions across various contexts, as well as to implementation and scale-up. This study aimed to use an expert consensus approach to select disease-related measures for type 2 diabetes to facilitate cross-contextual research, as well as the implementation and scaling-up of initiatives. Methods: The study was conducted using a two-phased cross-sectional design consisting of an online survey among research experts in 17 diabetes projects working in a global context, followed by an online modified Delphi panel comprised of reviewers with domain-specific expertise from different income settings who were not survey participants. Results: Out of 153 measures from 11 domains assessed, 49 were classified as core, 58 as optional, and 46 were excluded. The domains and measures spanned several categories, including demographics, medical history, medication adherence, health behaviors, anthropometric measures, biochemical measures, and quality-of-life-related issues. Conclusion: The core dataset of selected measures in type 2 diabetes may provide a standardized approach for determining which data should be collected. This can facilitate transnational comparisons between or within implementation projects to advance global diabetes research.
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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 | 0.546 | 0.519 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".