Preoperative staging of cancer of the pancreatic head: is there room for improvement?
Bibliographic record
Abstract
BACKGROUND: Despite advances in preoperative staging, cancer of the pancreatic head is frequently found to be unresectable at laparotomy. We sought to identify potential areas of improvement in preoperative staging. METHODS: We performed a retrospective institutional review of patients referred for resection of cancer of the pancreatic head over a 2-year period. The primary outcome was the rate of metastasis or unresectable disease found at laparotomy in patients who were booked for pancreaticoduodenectomy with curative intent. RESULTS: During the study period, 133 patients were referred with suspected cancer of the pancreatic head. All underwent preoperative computed tomography scanning. Twenty-four also underwent preoperative endoscopic ultrasonography (EUS) and 23 also underwent magnetic resonance imaging (MRI). In total, 78 patients were deemed not to be candidates for surgery, leaving 55 patients with potentially resectable cancer who were scheduled for pancreaticoduodenectomy. Of these, 32 patients (58%) underwent successful resection with curative intent, and 23 patients (42%) were found to have metastatic or locally advanced disease not identified by preoperative staging. Reasons for nonresectability were metastases (9 patients, 16%), vascular involvement (12 patients, 22%) and mesentery involvement (2 patients, 4%). One patient had a diagnostic laparoscopy immediately before planned open exploration and was found to have peritoneal seeding precluding curative resection. Of the patients who underwent EUS, 14 were not surgical candidates because of locally advanced tumours. Ten patients were offered surgery with curative intent, and 5 patients (50%) were found have unresectable tumours (4 metastatic, 1 locally advanced). Of the patients who underwent MRI, 11 were offered surgery, and 5 (45%) had unresectable tumours (2 metastatic, 3 locally advanced disease). CONCLUSION: In our institution, preoperative staging for cancer of the pancreatic head misses a substantial number of metastatic and unresectable disease. There is clearly room for improvement, and newer technologies should be evaluated to enhance the detection of metastatic and locally advanced disease to prevent unnecessary laparotomy.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".