Longitudinal toxicity analysis with novel summary metrics of lenalidomide maintenance in follicular lymphoma in ECOG-ACRIN 2408.
Bibliographic record
Abstract
6511 Background: Conventional adverse event (AE) analysis (ToxC) focuses on incidence of grade (gr) 3+ toxicities, and fails to capture AE time profile. Novel metrics that reflect chronic low gr and overall AE burden are needed. We applied the Toxicity over Time (ToxT) approach to ECOG-ACRIN 2408 to depict time-dependent toxicity of lenalidomide (L) with rituximab maintenance (MR) in follicular lymphoma (FL), and we developed a novel summary metric of symptomatic AE burden, the maximum gr over time (MGOT). Methods: In E2408, high risk FL patients (pts) were randomized (1:2:2) to: A) bendamustine-rituxumab (BR) x 6 then MR x 2 years (yrs) vs B) BR-bortezomib x 6 then MR x 2 yrs vs C) BR x 6 then MR x 2 yrs + L x 1 yr (MRL). Analysis included 3 laboratory and 5 symptomatic AEs of highest incidence during maintenance on arms A and C. Treatment-related AEs of any gr were analyzed by ToxC and ToxT. Repeated measures, time-to-event (TTE) and area under the curve (AUC) analyses capture trends over time in ToxT. MGOT combines the 5 symptomatic AEs. Results: 104 randomized pts (30 MR, 74 MRL) were included. For the laboratory AEs, by ToxC, neutropenia incidence was significantly higher in MRL (84%) than MR (47%, p < .001). ToxT additionally shows neutropenia does not worsen over time (10/14/20% gr 1/2/3+ at c1, 6/21/12% gr 1/2/3+ at c12). For the symptomatic AEs, ToxC indicates 2% gr 3+ GI AEs. However, gr 1-2 GI AEs are more common on MRL (59%) than MR (26%, p < .001). ToxT AUC captures a higher burden of GI AEs over time on MRL(2.8) vs MR(1.4, p = .002). TTE depicts sooner GI AE onset in MRL (10% vs 0% gr 2+ GI by day 50, p = 0.03). Bar charts of incidence and grade by cycle illustrate this improves over time (34/7/4% gr 1/2/3+ at c1, 13/0/0% gr 1/2/3+ at c12). ToxT MGOT analyses demonstrate earlier time to gr 2+ symptomatic AE on MRL vs MR (63% vs 31% by day 50, p < .001) and suggest that overall AE burden over time is higher for patients on MRL(AUC 18.2) than MR(11.8, p < .001). Conclusions: ToxT depicts AE time profile and can guide AE interventions. Summary metrics suggest that symptomatic AEs occur earlier and their burden over time is higher on MRL. We are implementing ToxT in patient-reported AE data to better characterize pt tolerability.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".