Additional file 1 of Predicting chemoresponsiveness in epithelial ovarian cancer patients using circulating small extracellular vesicle-derived plasma gelsolin
Bibliographic record
Abstract
Additional file 1: Supplementary Figure 1. Distribution of individual biomarkers between chemoresistant (PFI ≤ 6 months) and chemosensitive (PFI > 6 months) groups using dot plots. Points on dot plots represent individual patient biomarker concentrations. Line with error bars represent group mean and SEM. (A) CA125, Mann-Whitney U-test. (B) Total pGSN, Student t-test. (C) sEV-pGSN, Mann-Whitney U-test. Supplementary Figure 2. Kaplan-Meier survival analysis (log-rank test) between total pGSN, sEV-pGSN, or total pGSN/sEV-pGSN with DFS or OS. Cut-off values were determined using Fisher’s exact test. (A) Total pGSN, DFS. (B) Total pGSN, OS. (C) sEV-pGSN, DFS. (D) sEV-pGSN, OS. (E) Total pGSN/sEV-pGSN, DFS. (F) Total pGSN/sEV-pGSN, OS. Supplementary Figure 3. Distribution of multi-analyte biomarkers between chemoresistant (PFI ≤ 6 months) and chemosensitive (PFI > 6 months) groups using dot plots. Points on dot plots represent individual patient biomarker concentrations. Line with error bars represent group mean and SEM. (A) Total pGSN/sEV-pGSN. (B) Total pGSN/CA125. (C) sEV-pGSN/CA125. (D) (Total pGSN/sEV-pGSN)/CA125. Mann-Whitney U-test used for all four multi-analyte biomarkers. Supplementary Figure 4. Receiver operating characteristic curve analysis for individual and multi-analyte biomarkers to predict PFI ≤ 6 months. (A) Total pGSN, sEV-pGSN, and total pGSN/sEV-pGSN. (B) CA125, total pGSN/CA125, sEV-pGSN/CA125, and (total pGSN/sEV-pGSN)/CA125. Supplementary Figure 5. Characterization of sEVs. (A) Western blot of sEV surface markers (CD9, CD63, and CD81), a cytoplasmic marker (GAPDH), and a negative sEV marker (calnexin). (B) Electron micrograph illustrating pGSN within extracellular vesicles. (C) Size distribution curve from nanoparticle tracking analysis of 5 representative samples. Bars represent mean particle count and error bars represent SEM.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.826 | 0.099 |
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".