Testing remote feedback using a virtual semi-automated educational tool for the detection of pancreatic tumour-vessel contact on staging CT
Bibliographic record
Abstract
PURPOSE: Assessing tumour-vessel contact in pancreatic adenocarcinoma on CT is challenging for trainees and time-intensive for educators. Semi-automating feedback on this task may optimize radiologist time and standardize resident education. We hypothesized that residents who reviewed expert annotations of tumour-vessel contact would outperform those without feedback on an independent test set. METHODS: We retrospectively reviewed pre-operative staging CTs from 60 patients who underwent upfront surgical resection for pancreatic adenocarcinoma. Two resident groups (control and test) independently annotated tumour contact with the superior mesenteric artery. The test group received feedback-annotations from three expert radiologists-for the first 30 cases; the control group received none. Resident performance on the remaining 30 cases was compared against both surgical pathology and expert annotations. RESULTS: Test group residents demonstrated higher sensitivity than control group residents (mean sensitivity = 93 % vs. 79 %), with comparable specificity and accuracy relative to surgical pathology. While both groups performed similarly relative to expert consensus, the test group showed greater consistency in sensitivity (mean variation = 29 % vs. 46 %). CONCLUSION: Virtual expert feedback improved resident sensitivity in identifying tumour-vessel contact without compromising specificity or accuracy. These findings support the use of semi-automated educational tools to enhance radiology training efficiency and effectiveness.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".