CA-125 can be part of the tumour evaluation criteria in ovarian cancer trials: experience of the GCIG CALYPSO trial
Bibliographic record
Abstract
BACKGROUND: CA-125 as a tumour progression criterion in relapsing ovarian cancer (ROC) trials remains controversial. CALYPSO is a large randomised trial incorporating CA-125 (GCIG criteria) and symptomatic deterioration in addition to Response Evaluation Criteria in Solid Tumours (RECIST) criteria (radiological) to determine progression. METHODS: In all, 976 patients with platinum-sensitive ROC were randomised to carboplatin-paclitaxel (C-P) or carboplatin-pegylated liposomal doxorubicin (C-PLD). CT-scan and CA-125 were performed every 3 months until progression. RESULTS: In all, 832 patients (85%) progressed, with 60% experiencing a first radiological progression, 10% symptomatic progression, and 28% CA-125 progression without evidence of radiological or symptomatic progression. The benefit of C-PLD vs C-P in progression-free survival was not influenced by type of first progression (hazard ratio 0.85 (95% confidence interval (CI): 0.66-1.10) and 0.84 (95% CI: 0.72-0.98) for CA-125 and RECIST, respectively). In patients with CA-125 first progression who subsequently progressed radiologically, a delay of 2.3 months was observed between the two progression types. After CA-125 first progression, median time to new treatment was 2.0 months. In all, 81%of the patients with CA-125 or radiological first progression and 60% with symptomatic first progression received subsequent treatment. CONCLUSION: CA-125 and radiological tests performed similarly in determining progression with C-PLD or C-P. Additional follow-up with CA-125 measurements was not associated with overtreatment.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".