Real-World Outcomes of Advanced Soft Tissue Sarcoma Patients Treated with Pazopanib
Bibliographic record
Abstract
Background: Pazopanib is an oral multitarget tyrosine kinase inhibitor that is currently approved for the treatment of select subtypes of advanced Soft Tissue Sarcoma (STS) in patients who have progressed on prior anthracyclinebased chemotherapy regimens. In this study, we examine data from multiple centers to assess the efficacy of pazopanib in practice outside of a clinical trial setting. Methods: A retrospective chart analysis was conducted for pre-treated, advanced soft tissue sarcoma patients who began treatment with pazopanib in Alberta, Canada and Cairo, Egypt (2012-2018). Results: In total, 39 predominantly male (56.4%) patients received pazopanib. The median age was 51, 67% of whom had an ECOG of one or less. The predominant sarcoma subtype was leiomyosarcoma (30.8%), and all patients had received at least one prior line of systemic therapy. Thirtytwo of the 39 patients (82%) were initially given the full dose of 800mg with a median time on treatment of 116 days. Seven of the 39 (18%) patients required a dose reduction while on treatment. A majority (94.9%) of patients ultimately discontinued pazopanib treatment for reasons including death (21.6%), disease progression (62.2%), and toxicity (16.4%). The median progression-free and overall survival for these patients was 4.1 months (95%CI, 3.6-4.5) and 8.4 months (95% CI, 4.3-12.5), respectively. Conclusion: Pazopanib is an efficient and generally well-tolerated oral systemic therapy for the treatment of advanced, pre-treated, non-adipocytic soft tissue sarcoma. These results show the efficacy of pazoponib outside of a clinical trial setting.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".