Impact of deintensifying hypoglycaemic drugs in older adults with type 2 diabetes: protocol for an emulation of a target trial
Bibliographic record
Abstract
INTRODUCTION: In older adults with type 2 diabetes (T2D), overtreatment with hypoglycaemic drugs (HDs: sulfonylureas, glinides and/or insulins) is frequent and associated with increased 1-year mortality. Deintensification of HD is thus a key issue, for which evidence is though limited. The primary objective of this study will be to estimate the effect of deintensifying HD on clinical outcomes (hospital admission or death) within 3 months in older adults (≥75 years) with T2D. METHODS: We will emulate with real-world data a target trial, within The Health Improvement Network cohort, a large-scale database of data collected from electronic medical records of 2000 general practitioners in France. From 1 January 2010 to 28 February 2019, we will include eligible patients ≥75 years who will have T2D, a stable dose of HDs, glycated haemoglobin A1c (HbA1c) value <75 mmol/mol (9.0%) and no deintensification in the past year. The target trial will be sequentially emulated (ie, eligibility assessed) every month in the database. Patients will be classified at baseline of each sequential trial in the intervention arm (deintensification of HDs: decrease of ≥50% in the total dose of HDs, including complete cessation) or control arm (no deintensification of HDs). The pooled dataset for all sequential emulated trials will be analysed. The primary outcome will be time to first occurrence of hospital admission or death, within 3 months. Secondary outcomes will be hospitalisation, death, appropriateness of glycaemic control and occurrence of HbA1c >75 mmol/mol within 1 year. Participants will be followed from baseline to 12 months after randomisation, administrative censoring, or death, whichever occurs first. A pooled logistic regression will be used to estimate the treatment effect on the incidence of the outcomes. DISSEMINATION AND ETHICS: No ethical approval is needed for using retrospectively this fully anonymised database. The results will be disseminated during conferences and through publications in scientific journals.
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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 | 0.059 | 0.055 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.015 |
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".