Evaluating the influence of health policy on health outcomes: REALITI-A post hoc analysis
Bibliographic record
Abstract
Background: Mepolizumab treatment reduced the rate of clinically significant exacerbations (CSE) in patients with severe asthma in the REALITI-A study. However, differences between country reimbursement criteria may impact patient selection and outcomes. Aims and Objectives: This post hoc analysis of REALITI-A assessed the impact of country reimbursement criteria on clinical outcomes at 1 year. Methods: Countries were grouped based on reimbursement criteria restriction (least restrictive [Belgium and USA], moderately restrictive [Canada, Germany, Italy and Spain], most restrictive [UK]). Rate ratios (RRs) of CSE were assessed at 1 year post-mepolizumab treatment compared with 1 year pre-treatment for each country. RRs of CSE in the most and moderately restrictive groups were compared with the least restrictive group. Results: Reductions in CSE were observed across all countries post-mepolizumab treatment and across all reimbursement criteria groups. Compared with the least restrictive group, the moderately restrictive group had significantly greater reduction in the RR of CSE (rate difference [log RR; 95% confidence interval]: -0.43 [−0.73; -0.14]; p<0.0042); there was an increase in the RR of CSE in the most restrictive group (0.15 [−0.13; 0.44]; p=0.2786; Figure). Conclusions: Country reimbursement criteria may impact the treatment benefit of mepolizumab, influencing clinical outcomes. Funding: GSK (204710) erj;64/suppl_68/PA1182/F1 F1 F1
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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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".