55 Impact of Staging Investigations on Nodal Upstaging in Early Oesophago-Gastric Adenocarcinoma: Multi-Centre CONGRESS Dataset Analysis
Bibliographic record
Abstract
Abstract Aim Current recommendations for the clinical staging of patients undergoing resection for early oesophago-gastric (OG) cancer are variable and the value of staging investigations is unclear. The aim of this study was to assess current practice for staging early OG cancers across the UK, and the accuracy of staging with reference to nodal disease at surgery. Method Data for surgical patients was extracted from the CONGRESS database, a large UK-based multi-centre dataset for patients with T1N0 OG cancer between 2015 and 2022. Logistic regression analysis was performed to assess the association of different staging investigations on subsequent nodal upstaging. Cox regression analysis was used to analyse for impact on overall survival (OS). Results 497 patients from 28 centres were included, 13.1% of which underwent N upstaging from clinical to pathological staging. The rate of unexpected LNM was 12.7% in patients who underwent a CT pre-treatment, compared to 18.2% in patients with no staging investigations. Patients that underwent no staging investigations were also more likely to have unexpected nodal metastases at surgery (OR 6.66 (95%CI 1.34-33.24), p=0.021). The addition of PET-CT, EUS and staging laparoscopy had no significant impact on N upstaging (p=0.062, 0.053, and 0.690 respectively). No combination of staging modality had a significant impact on OS. Conclusions Current guidelines are variable in their recommendation of pre-operative staging investigations for early OG cancer. This study suggests CT plays an important role in the staging of this population. Other staging modalities could be considered selectively, rather than routinely, to preserve resources and accelerate treatment pathways.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".