GD2 and GD3 gangliosides as prognostic biomarkers in high grade serous ovarian cancer
Bibliographic record
Abstract
OBJECTIVE: Gangliosides GD2 and GD3 have been proposed to be of significance in diagnosis of ovarian masses. We aim to study serum GD2 and GD3 gangliosides as predictors of oncological outcomes among high grade serous (HGS) ovarian cancer (OC). MATERIALS AND METHODS: A retrospective study including biobanked serum samples of HGS OC treated between 2005 and 2016. Serum GD2 and GD3 concentrations were quantified using indirect ELISA and analyzed with respect to survival. RESULTS: Sixty patients were included. Patients with GD3>12.8 ng/mL had shorter PFS when compared to patients with lower level; median 31 vs. 67 months, p = 0.005. Patients with GD2> 7.1 ng/mL had shorter median PFS than those with lower level of (23 vs. 52 months, p = 0.024.) Patients with GD3>14.5 ng/mL had shorter OS vs. patients with lower level (median 31 vs. 70 months, p = 0.002). In a Cox regression, following adjustment for age, CA-125, disease stage and age, serum elevated GD3 was independently associated with short PFS (adjusted hazard ratio 2.0, 95 % CI 1.1-3.8, p=.024). In a separate Cox regression, elevated GD2 was independently associated with PFS (adjusted hazard ratio3.0 (1.2-7.7). p=.019. High serum GD3 and GD2 were independently associated with short OS as well. CONCLUSIONS: High levels of serum GD2 and GD3 in HGS OC were associated with shorter PFS and OS. GD3 is superior to GD2 as a biomarker for prognosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".