EURAMOS-1 study: Recruitment, characteristics, and initial treatment of more than 2,000 patients (pts) with high-grade osteosarcoma.
Bibliographic record
Abstract
10081 Background: EURAMOS is a transAtlantic collaboration formed to improve survival in osteosarcoma by conducting RCTs in a clinically relevant timeframe. EURAMOS-1, the largest study conducted in this rare cancer, has completed accrual. It includes two randomized comparisons investigating treatment optimization on the basis of histological response to neoadjuvant chemotherapy. Methods: Pts ≤40yrs with resectable, high grade extremity or axial osteosarcoma were eligible for registration. All were planned for 2 cycles of neoadjuvant methotrexate, doxorubicin, cisplatin (MAP) then surgical resection of the primary tumour. Pts with complete macroscopic resection and no disease progression were eligible for randomization: [i] “good responders”, <10% viable tumor, MAP +/- 18m maintenance pegylated interferon; [ii] “poor responders”, ≥10% viable tumor, MAP vs MAPIE (MAP + ifosfamide, etoposide). Target sample size was ~1,260 pts randomized requiring ~2000 pts registered estimating a non-randomization rate of 30-35%. Results: From Apr 2005 to Dec 2011, 2,260 pts were registered and 1,332 randomized from 17 countries, 320 sites: median age 14yrs (IQR 11-17); 59% male; 50% femoral site; 23% definite/possible metastases; 92% conventional osteosarcoma (diagnostic biopsy). At Sep 2011 planned IDMC review, neoadjuvant treatment data were known for 2024 pts; 1926 (95%) completed 2 cycles pre-operative MAP with 3 treatment related deaths. 59% pts were randomized; non-consent was the most frequent reason for non-randomization. Relative to expected age-specific incidence (UK NCIN 1979-2007) there was apparent under recruiting of older pts: females ≥15yrs and all pts >19yrs, with greater under recruitment of females than males <35yrs. Pts 20-29yrs were less likely to be randomized than those aged 5-19 and ≥30 yrs, 52% vs 57-62%. Conclusions: EURAMOS-1 demonstrates that large RCTs are feasible in rare cancers with inter-continental collaboration and is a model for future trials. Neoadjuvant MAP was safe in a geographically diverse cohort. Improving clinical trial access and randomization rates for young people is still required. Multiple funders detailed at bit.ly/ymUR9w.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".