A risk stratification model for toxicities in phase 1 immunotherapy trials
Bibliographic record
Abstract
INTRODUCTION: Despite the increased number of novel immunotherapy (IO) agents under current development, their toxicity profile remains to be fully elucidated. METHODS: An IO risk stratification model was developed based on 5 different variables: treatment-related deaths; rate of grade ≥3 treatment-related adverse events or treatment-emergent adverse events; grade ≥2 encephalopathy or central nervous system toxicity; grade ≥2 cytokine release syndrome; and the number and type of dose-limiting toxicity. Phase 1 IO trials published from January 2014 to December 2020 were reviewed and categorised based on our risk stratification model into three categories: low-, intermediate- and high-risk. Clinical trial variables were associated with the high-risk category. To review the quality of reporting across phase 1 IO trials, a subset of studies was further examined by the use of the ASCO/SITC Trial Reporting in Immuno-Oncology (TRIO) standards. RESULTS: Different IO compounds demonstrated diverse risk profiles. In multivariable analysis, combination versus IO single agent treatment, and testing IO agents different from anti-programmed death-1/programmed death ligand-1 (anti-PD1/L1), anti-cytotoxic t-lymphocyte antigen-4 (anti-CTLA4) antibodies and anti-cancer vaccines were associated with a higher toxicity risk. None of the studies examined in our dataset reported all the items included in the TRIO standards. CONCLUSIONS: Our results have important implications for future clinical trial design. Additionally, standards for reporting are urgently needed.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".