Benefit of Avasopasem Manganese on Severe Oral Mucositis in Head and Neck Cancer in the ROMAN Trial: Unplanned Secondary Analysis
Bibliographic record
Abstract
Purpose Oral mucositis (OM) is a debilitating side effect of cisplatin and intensity-modulated radiation therapy (IMRT) in patients with head and neck cancer. The phase 3 ROMAN trial showed avasopasem manganese (AVA) significantly decreased individual endpoints of incidence and duration of severe oral mucositis (SOM, World Health Organization [WHO] grade 3-4), with nominal decrease in severity (WHO grade 4) and significant increase in the delay in onset of SOM. We sought to determine the Net Treatment Benefit (NTB) of AVA versus placebo (PBO) using the generalized pairwise comparisons (GPC) method. Methods and Materials GPC is a statistical method that permits simultaneous analysis of several prioritized outcomes, comparing all possible pairs of a patient in the active (ie, AVA) group and a patient from the control (ie, PBO) group. NTB is the net benefit across all the outcomes for AVA compared to PBO. Key clinically relevant outcomes from ROMAN were prioritized: (1) WHO grade 4 OM incidence; (2) SOM incidence; (3) days of SOM; (4) days to SOM onset, with 7 days difference defined as the clinical relevance threshold for SOM days and SOM onset. Results GPC analysis of 407 patients (AVA = 241, placebo=166) stratified by cisplatin schedule and treatment setting resulted in 13,969 pairwise comparisons. AVA showed statistically significant net benefit on all 4 key outcomes with a 53.9% probability that AVA would benefit patients versus a 35.0% probability that PBO would; the difference between these probabilities was a NTB of 18.9% ( P = .0012), translating to an AVA number needed to treat of 5.3 patients. All outcomes contributed to NTB, reflecting improvements in SOM incidence, onset and duration, and in grade 4 OM incidence seen in the original ROMAN analysis. Conclusions This GPC analysis shows compelling evidence from the ROMAN trial of AVA's clinical benefit across key parameters of SOM burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".