CA‐125 reduction during neoadjuvant chemotherapy is associated with success of cytoreductive surgery and outcome of patients with advanced high‐grade ovarian cancer
Bibliographic record
Abstract
INTRODUCTION: The objective was to assess whether an early response to neoadjuvant chemotherapy in women with advanced ovarian cancer may predict short- and long-term clinical outcome. MATERIAL AND METHODS: This is a retrospective study of all women with stage III-IV tubo-ovarian cancer treated with neoadjuvant chemotherapy at a single center in Montreal between 2003 and 2014. Logistic regression models were used to evaluate the association between cancer antigen 125 (CA-125) levels during neoadjuvant chemotherapy and debulking success. Cox proportional hazard models were used to estimate hazard ratios and their respective 95% CI for death and recurrence. Harrell's concordance indices were calculated to evaluate which variables best predicted the chemotherapy-free interval and overall survival in our population. RESULTS: In all, 105 women were included. Following the first, second, and third cycles of neoadjuvant chemotherapy, CA-125 levels had a median reduction of 43.2%, 85.4%, and 92.9%, respectively, compared with CA-125 levels at diagnosis. As early as the second cycle, CA-125 was associated with overall survival (hazard ratio 1.03, 95% CI 1.01-1.05, per 50 U/mL increment). By the third cycle, CA-125 did not only predict overall survival (hazard ratio 1.04, 95% CI 1.01-1.08), but it predicted overall survival better than the success of debulking surgery (Harrell's concordance index 0.646 vs 0.616). Both absolute CA-125 levels and relative reduction in CA-125 levels after 2 and 3 cycles predicted the chance to achieve complete debulking (P < .05). CONCLUSIONS: Reduction of CA-125 levels during neoadjuvant chemotherapy provides an early predictive tool that strongly correlates with successful cytoreductive surgery and long-term clinical outcome in women with advanced high-grade serous and endometrioid ovarian cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.001 | 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".