The prognostic value of perioperative, pre‐systemic therapy <scp>CA</scp> 125 levels in patients with high‐grade serous ovarian cancer
Bibliographic record
Abstract
Abstract Objective To investigate the ability of preoperative CA 125 and post‐surgical CA 125 changes to predict outcomes among patients with high‐grade serous ovarian cancer ( HGSC ). Methods The present retrospective cohort study included patients with HGSC who underwent surgery between January 1, 2003, and December 31, 2011 at Princess Margaret Cancer Center, Toronto, ON , Canada. CA 125 was measured at diagnosis and following surgery, and the CA 125 ratio was calculated (preoperative CA 125/postoperative CA 125). Optimal CA 125 cutoff levels were identified using the point with the most significant log‐rank‐test result. Univariate and multivariate analyses with Cox proportional hazard modeling was used to study overall survival. Results Among 212 patients, an optimal baseline CA 125 cutoff value of 174 U/ mL and a seven‐fold decrease in CA 125 after surgery were positive prognostic indicators. A 10‐fold increase in baseline CA 125 was associated with decreased overall survival (univariate hazard ratio 1.55, 95% confidence interval [ CI ] 1.17–2.06; P =0.002; multivariate hazard ratio 1.72, 95% CI 1.21–2.44; P =0.002). An increase in the CA 125 ratio (log 10 [preoperative CA 125/postoperative CA 125]) was associated with improved overall survival (univariate hazard ratio 0.63, 95% CI 0.43–0.90; P =0.012; multivariate hazard ratio 0.41, 95% CI 0.24–0.70, P <0.001). Conclusion CA 125 demonstrated prognostic value for HGSC ; baseline CA 125 of 174 U/ mL or lower and a post‐surgical decline of seven‐fold or greater were associated with improved overall survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".