Diagnosis and surgical treatment of solid pseudopapillary neoplasm of the pancreas: analysis of 24 cases
Bibliographic record
Abstract
BACKGROUND: Our aim was to summarize our experience with the diagnosis and surgical treatment of solid pseudopapillary neoplasm (SPN) of the pancreas to provide a reference for the management of this rare condition. METHODS: We collected and analyzed retrospective data on the clinical presentation, laboratory investigations, radiologic imaging, pathology and operative details of patients with SPN of the pancreas diagnosed between February 2001 and December 2009. RESULTS: In all, 23 of 24 patients were women, and the mean age of all patients was 31 years. The most common clinical presentation was vague abdominal pain. Abdominal imaging showed solid or solid cystic masses in the pancreas, mostly in the tail or head of the gland. All patients were treated surgically. There were no postoperative deaths. After follow-up ranging from 4 to 109 months (median 68 mo), 20 of 22 patients who underwent curative resection were alive with no evidence of disease recurrence. Of the 2 patients with R1 resections, 1 died 42 months after surgery, whereas the other underwent a second operation and was alive after 36 months' follow-up. CONCLUSION: Solid pseudopapillary neoplasm of the pancreas is a relatively indolent tumour. The initial diagnosis of SPN of the pancreas is suggested by radiologic imaging findings but should be considered in the context of clinical and histopathologic characteristics. We advocate for complete surgical resection once SPN is diagnosed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".