Early Decline in Cancer Antigen 125 as a Surrogate for Progression-Free Survival in Recurrent Ovarian Cancer
Bibliographic record
Abstract
We used data from 886 patients from the CAELYX in Platinum Sensitive Ovarian Patients (CALYPSO) trial, recruited between April 2005 and September 2007, to examine the role of early decline in cancer antigen 125 (CA125) and early tumor response as prognostic factors and surrogates for superiority of treatment with carboplatin-pegylated liposomal doxorubicin (CPLD) compared with carboplatin-paclitaxel (CP) in a landmark analysis. Progression-free survival (PFS) was estimated by Kaplan-Meier analyses. We used univariate and multivariable Cox proportional hazards analyses to assess early decline and early response as surrogates for CPLD treatment benefit compared with CP. All statistical tests were two-sided. Early decline (defined as rate of CA125 decrease of at least 50% per month) was associated with improved PFS (adjusted hazard ratio [HR] for progression = 0.81, 95% confidence interval [CI] = 0.67 to 0.97, P = .02) but early response (complete or partial responses) was not. CPLD was associated with improved PFS compared with CP (HR = 0.82, 95% CI = 0.69 to 0.96, P = .01). However, fewer CPLD patients had an early decline (161 [37.4%] vs 233 [51.2%], P < .001) or an early response (146 [33.9%] vs 176 [38.7%], P = .14) compared with CP patients. The PFS for CPLD patients did not change statistically significantly after adjustment for early decline (adjusted HR = 0.80, 95% CI = 0.68 to 0.94, P = .007). These findings are opposite to what would be expected if these markers were good surrogates for treatment benefit.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".