Protocol for the development of a core outcome set for type 1 diabetes risk screening
Bibliographic record
Abstract
INTRODUCTION: Type 1 diabetes is a chronic autoimmune disease that often presents with diabetic ketoacidosis at diagnosis. Since detection of type 1 diabetes risk is possible using genetic risk scores and autoantibody assays, prevention of diabetic ketoacidosis or delayed onset of type 1 diabetes may be possible and may improve outcomes. Several pilot screening programmes for type 1 diabetes risk have emerged worldwide but outcomes measured in these screening programmes are heterogeneous, making it difficult to compare and synthesise findings across studies. To improve the standardisation of outcome reporting and measurement, we aim to develop a patient-oriented core outcome set for studies of type 1 diabetes risk screening. METHODS AND ANALYSIS: This five-step protocol was developed in alignment with the COS-STAndardised Protocol Statement and the Core Outcome Measures in Effectiveness Trials framework. The five steps will include: (1a) conducting a rapid literature review, (1b) gathering input on candidate outcomes from members of the public, (2) combining literature and public input to prepare a preliminary list of outcomes, (3) conducting Delphi surveys with a range of stakeholders to begin to establish consensus on outcomes, (4) holding a final consensus meeting to establish consensus on outcomes and (5) establishing the outcome measurement instruments for the core outcome set. ETHICS AND DISSEMINATION: Ethics approval has been provided by The Hospital for Sick Children Research Ethics Board. The core outcome set will be distributed to researchers and clinicians involved in diabetes screening and clinical care, patient and family networks, research funders, journal editors, public health experts, and policymakers. Disseminated materials will be tailored to the various end users in the form of publication through academic journals, policy briefs, conferences, educational webinars, websites and social media.
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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 | 0.245 | 0.309 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.104 | 0.028 |
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".