Toxicity-benefit analysis of advanced prostate cancer trials using weighted toxicity scoring.
Bibliographic record
Abstract
110 Background: The weighted toxicity score (WTS) is a metric suitable for comparing the toxicity burden in experimental versus control arms of randomized controlled trials (RCTs). When compared against clinical endpoints, the WTS can be used to evaluate the cost versus benefit of anti-cancer agents. This study sought to apply the WTS to prostate cancer (PC) clinical trials. Methods: Select phase 3 PC RCTs with reported adverse event (AE) data were compiled. The WTS was computed for each trial arm and compared to reported hazard ratios (HRs) of primary and/or secondary endpoints (i.e., overall survival (OS) and progression-free survival (PFS)). Average WTSs for the experimental versus control arms were used to calculate the toxicity differential between treatment arms. Average HRs for OS and PFS were used to compare efficacy. Results: Sixteen RCTs were analyzed (investigational agents: androgen-receptor signaling inhibitors (ARSi) [n = 4], poly (ADP-ribose) polymerase inhibitors (PARPi) monotherapy [n = 3], ARSi + PARPi as well as ARSi + ARSi combination therapies [n = 3 each], and triple therapy [n=3]). The median number of distinct reported AEs across all studies was 18 (range, 9-61). Overall, toxicity and efficacy were greater among experimental than control arms (6.62 versus 4.00 median WTS; median HR for OS 0.71, median HR for PFS 0.61). The triple therapy studies observed the lowest increase in toxicity by adding an ARSi to control therapy (10.4%), associated with 52% lower risk of progression, and 29% lower risk of death. Comparably, the ARSi + PARPi trials noted the highest increase in toxicity (79.7%), with 32% lower risk of progression and 12% lower risk of death. The PARPi monotherapy, ARSi monotherapy, and ARSi + ARSi combination therapy studies reported increased toxicity due to experimental therapy in descending order (Table). Conclusions: The WTS enables toxicity versus benefit assessment for anti-cancer regimens. Clinical application of this metric may facilitate individualized treatment planning in advanced PC management. [Table: see text]
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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 | 0.056 | 0.313 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads 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".