Reducing clinical trial eligibility barriers for patients with MDS: an icMDS position statement
Bibliographic record
Abstract
ABSTRACT: Excessively restrictive inclusion and exclusion criteria in clinical trials are one of many barriers to clinical trial enrollment for patients with myelodysplastic syndromes/neoplasms (MDSs). Many organizations are developing efforts to increase clinical trial eligibility; yet, several recent publications focused on patients with MDS suggest that many patients with this disease may be excluded from clinical trials unnecessarily. Clinical trial eligibility should reflect the phase of the study and risks of the agent being studied. Phase 3 trials should be less restrictive than early-phase trials to represent the real-world population as closely as possible. We hypothesize that many clinical trials, particularly phase 3 trials, have unnecessarily restrictive eligibility criteria. This study aims to evaluate the most common eligibility criteria according to phase of trial and to determine whether criteria correspond with drug safety signals. We identified MDS clinical trials registered on ClinicalTrials.gov from 1 January 2000 to 1 September 2023 and analyzed the eligibility criteria of 191 therapeutic MDS trials. We found that categorical inclusion and exclusion criteria are remarkably similar in representation across trial phases. Additionally, only 13% of trials are concordant with drug safety signals, suggesting that the eligibility criteria are often arbitrary. On behalf of the icMDS (International Consortium for Myelodysplastic Syndromes), an association of international MDS experts, we provide a position statement on restrictive eligibility criteria for MDS clinical trials that should be avoided with the aim of removing barriers to clinical trial enrollment.
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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 | 0.511 | 0.526 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.026 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".