Metastatic lymph node impostor in pancreatic cystadenocarcinoma.
Bibliographic record
Abstract
CONTEXT: Lymph node involvement in pancreatic cancer is a predictor of poor patient long-term survival. The detection of multiple metastatic peri-pancreatic nodes by EUS-FNA may dissuade the surgeon from undertaking a curative pancreatic resection. CASE REPORT: We report an interesting case of a man with chronic lymphocytic leukemia, who presented with the diagnostic problem of a pancreatic solid-cystic lesion and multiple malignant-looking peri-pancreatic lymphadenopathy on EUS. EUS-FNA yielded chronic lymphocytic leukaemia involvement in the peri-pancreatic lymph nodes and a markedly elevated CEA in the cystic fluid. The absence of adenocarcinoma involvement of the lymph nodes prompted surgery on the pancreatic lesion with a curative intent. Pancreatic mucinous cystadenocarcinoma was diagnosed and a sub-total pancreatectomy was performed with clear resection margins. All 30 resected peri-pancreatic lymph nodes showed chronic lymphocytic leukemia involvement only. CONCLUSIONS: This case illustrates that abnormal lymphadenopathy adjacent to a primary pancreatic lesion may not necessarily be due to the latter. Systemic lymphoproliferative disease, as in this case, can masquerade as metastatic adenocarcinoma lymph nodes on EUS. EUS-FNA is useful in diagnosing lymphoproliferative disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".