Influence of tumor size and Eastern Cooperative Oncology Group performance status (ECOG PS) at baseline on patient (pt) outcomes in lenvatinib-treated radioiodine-refractory differentiated thyroid cancer (RR-DTC).
Bibliographic record
Abstract
6081 Background: In SELECT, lenvatinib significantly improved progression-free survival (PFS) of pts with RR-DTC versus placebo (18.3 v 3.6 months; hazard ratio [HR]: 0.21 [99% CI: 0.14, 0.31]; P<0.001). Here we examine the treatment of RR-DTC with lenvatinib in relation to tumor size (sum of all targeted lesions) and ECOG PS. Methods: In this post hoc analysis of SELECT with pts randomized to receive lenvatinib, Kaplan-Meier estimates of time to ECOG PS ≥2 were calculated for subgroups of pts according to baseline ECOG PS or tumor size. Objective response rate (ORR) and Kaplan-Meier estimates of overall survival (OS) and PFS according to ECOG PS (0 or 1) at baseline were calculated. Correlations between ECOG PS at baseline (0 or 1) and maximum tumor shrinkage were calculated using one-way analysis of variance. Results: Pts with ECOG PS 0 or 1 at baseline had similar demographic and disease characteristics. ORR was 78.5% and 51.0% for pts with ECOG PS 0 and 1 at baseline, respectively (odds ratio [95% CI]: 3.508 [2.018, 6.097]). Mean maximum percent decrease in tumor size was significantly greater in pts with baseline ECOG PS 0 (-46.13%) versus pts with ECOG PS 1 (-37.16%; P=0.0017). For pts with ECOG PS 1 at baseline, time to ECOG PS ≥2 was numerically shorter with tumor size >60 mm versus tumor size ≤60 mm (HR [95% CI]: 1.450 [0.708, 2.967]). Additional results are summarized in the table. Conclusions: Among pts with RR-DTC, PFS, OS, ORR, and time to ECOG ≥2 were generally better for patients with lower ECOG PS or smaller tumor size at baseline. These results may indicate that it is beneficial to start lenvatinib in pts with RR-DTC early, before ECOG PS worsens and tumor size increases. Clinical trial information: NCT01321554. [Table: see text]
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".