GATA6 immunohistochemistry and prognosis after surgical resection of pancreatic adenocarcinoma: results from the ESPAC-4 trial
Bibliographic record
Abstract
Background No prognostic biomarker is currently used in clinical management of patients with surgically resected pancreatic cancer other than CA-19-9. In this study, we tested the prognostic value of GATA6 measured with immunohistochemistry and digital assistance. Patients and methods One hundred and ninety-three patients with resected pancreatic ductal adenocarcinoma from the ESPAC-4 trial of adjuvant gemcitabine and capecitabine were included. Two pathologists independently assessed GATA6 protein expression by immunohistochemistry in tissue microarray cores, manually and with digital assistance. Overall survival was compared across GATA6 levels using multivariate Cox proportional hazard regressions, with exploratory analyses evaluating recurrence-free survival and differential treatment effects. Results Interobserver concordance improved with digitally assisted scoring (kappa 0.72 versus 0.25, P < 0.001). Median overall survival was 24.3 months [95% confidence interval (CI) 19.2-32.1 months] with low GATA6 expression versus 35.2 months (95% CI 29.9-53.0 months) with high GATA6 expression (adjusted hazard ratio 1.60, 95% CI 1.08-2.38, P = 0.02). Similar results were observed for recurrence-free survival (adjusted hazard ratio 1.45, 95% CI 0.99-2.14, P = 0.06). GATA6 expression was not associated with differential treatment effects. Conclusions GATA6 expression measured by immunohistochemistry with digital assistance was a prognostic biomarker among people with pancreatic adenocarcinoma treated with surgical resection and adjuvant chemotherapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".