Endoscopic biopsies of duodenal polyp/mass lesions: a surgical pathology review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Endoscopic biopsies of duodenal polyp/mass lesions are uncommon surgical pathology specimens. A surgical review with a report on three unusual duodenal polyp/mass lesions is presented. METHODS: A computer-based data search of duodenal polyp/mass lesions was conducted at the Saskatoon Health Region using the Lab Information System from 1996 to 2009. The source codes used included DUOBX and DUO. Surgical material on the retrieved cases was reviewed. RESULTS: The three index cases included duodenal polyp/mass lesions, which on primary analysis were diagnosed as 'poorly differentiated' carcinomas with some unusual features. The accurate diagnoses of metastatic renal cell carcinoma, metastatic phaeochromocytoma and metastatic malignant melanoma, respectively, were confirmed with retrospective analysis of previous clinical and pathological records. 130 duodenal polyp/mass related lesions were identified. 33% of these lesions were malignant and 67% were benign/normal. The majority of these biopsies originated in patients aged 60-79 years. Malignant lesions were more common in men (61%) than women (39%). 88% of the malignant cases were of carcinomatous origin. 16.3% of the carcinomas were reclassified as metastatic lesions arising from lung, breast, colon and pancreas. 41% of the benign cases had no significant pathological abnormalities. The remainder were predominantly adenomatous (14.9%) and inflammatory (13.8%) in origin. CONCLUSIONS: Endoscopic biopsies of duodenal polyp/mass lesions remain an uncommon specimen (0.01% in the authors' surgical pathology practice). Nevertheless, accurate identification of the exact pathology, even in 'poorly differentiated' high-grade carcinomas is advocated, as metastatic lesions will require specific treatment plans in conjunction with treatment of their primary tumour.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".