Gemcitabine Re-challenge in Metastatic Soft Tissue Sarcomas: A Therapeutic Option for Selected Patients
Bibliographic record
Abstract
BACKGROUND/AIM: Treatment options for patients with metastatic soft tissue sarcomas are limited. Re-challenge with a previously successful gemcitabine-based regimen is common. There are no published data to support this practice. PATIENTS AND METHODS: We conducted a retrospective search to identify patients re-challenged with gemcitabine-based chemotherapy (GBC) from 2003 to 2015. RESULTS: Twenty-nine patients re-challenged with gemcitabine were identified. The response rate for initial GBC was 55% (n=15) and for re-challenge GBC 26% (n=6). The median progression-free survival was 11.1 months (95%CI=7.2-11.9) for initial GBC and 5.3 months (95%CI=2.0-7.5) for re-challenge GBC. Overall survival following gemcitabine re-challenge was 12.2 months (95%CI=7.0-18.2). Twelve out of 26 evaluable patients (46%) treated with re-challenge GBC experienced grade 3-4 adverse events (CTCAE 4.03) with 31% (n=8) of patients requiring dose reduction. CONCLUSION: In selected patients, gemcitabine re-challenge can be considered in advanced sarcomas, however, this approach is associated with toxicity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".