Review of real-world evidence studies in type 2 diabetes mellitus: Lack of good practices
Bibliographic record
Abstract
OBJECTIVES: Unlike randomized controlled trials, lack of methodological rigor is a concern about real-world evidence (RWE) studies. The objective of this study was to characterize methodological practices of studies collecting pharmacoeconomic data in a real-world setting for the management of type 2 diabetes mellitus (T2DM). METHODS: A systematic literature review was performed using the PICO framework: population consisted of T2DM patients, interventions and comparators were any intervention for T2DM care or absence of intervention, and outcomes were resource utilization, productivity loss or utility. Only RWE studies were included, defined as studies that were not clinical trials and that collected de novo data (no retrospective analysis). RESULTS: The literature search identified 1,158 potentially relevant studies, among which sixty were included in the literature review. Many studies showed a lack of transparency by not mentioning the source for outcome and exposure measurement, source for patient selection, number of study sites, recruitment duration, sample size calculation, sampling method, missing data, approbation by an ethics committee, obtaining patient's consent, conflicts of interest, and funding. A significant proportion of studies had poor quality scores and was at high risk of bias. CONCLUSIONS: RWE from T2DM studies lacks transparency and credibility. There is a need for good procedural practices that can increase confidence in RWE studies. Standardized methodologies specifically adapted for RWE studies collecting pharmacoeconomic data for the management of T2DM could help future reimbursement decision making in this major public health problem.
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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 | 0.385 | 0.762 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.036 | 0.030 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".