Comment on: Efficacy and safety of various repeat treatment dosing regimens of rituximab in patients with active rheumatoid arthritis: results of a Phase III randomized study (MIRROR): reply
Bibliographic record
Abstract
Sir, We would like to thank Conway et al. [1] for their interest in our article [2] and their comments, which raise important aspects of the reporting of clinical trials. The mis-randomizations resulted from the Interactive Voice Response System (IVRS) vendor failing to update the medication list following a protocol amendment, which consequently resulted in the medication list not being synchronized with the randomization schedule. In practice, this caused the IVRS to allocate a medication pack containing a different regimen to that specified in the randomization schedule, and resulted in 60 patients (16% of the study population) being administered a rituximab regimen that was inconsistent with their randomized schedule. The blinding process itself, however, was unaffected and neither the sponsor, investigators nor patients were aware of the rituximab regimen being administered to patients. As a result of these mis-randomizations, a number of analysis populations were considered, including intent-to-treat (ITT)-as randomized, ITT-as treated, ITT-mis-randomizations excluded and a formal per-protocol (excluding mis-randomizations and other major protocol violators). The primary analysis [American College of Rheumatology (ACR) 20 at Week 48] was performed on all these populations and showed consistent outcomes with no difference between the treatment arms being observed in any analysis population. Given that the majority of mis-randomized patients still resulted in patients receiving a protocol-defined regimen (A–C) we decided to use the ITT-as treated population for the remaining efficacy analyses for several reasons. These included maximizing available patient data and, therefore power, as well as permitting efficacy and safety profiles to be reported in the same patient populations.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.042 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".