Implications of pregnancy on MCN of the pancreas: A multicentric case-control study
Bibliographic record
Abstract
BACKGROUND: Mucinous cystic neoplasms (MCN) of the pancreas express estrogen and progesterone receptors. Several case reports describe MCN increasing in size during gestation. The aim of this study is to assess if pregnancy is a risk factor for malignant degeneration of MCN. METHODS: All female patients who underwent pancreatic resection of a MCN between 2011 and 2021 were included. MCN resected or diagnosed within 12 months of gestation were defined perigestational. MCN with high grade dysplasia or an invasive component were classified in the high grade (HG) group. The primary outcome was defined as the correlation between exposure to gestation and peri-gestational MCN to development of HG-MCN. RESULTS: The study includes 176 patients, 25 (14 %) forming the HG group, and 151 (86 %) forming the low grade (LG) group. LG and HG groups had a similar distribution of systemic contraceptives use (26 % vs. 16 %, p = 0.262), and perigestational MCN (7 % vs 16 %, p = 0.108). At univariate analysis cyst size ≥10 cm (OR 5.3, p < 0.001) was associated to HG degeneration. Peri gestational MCN positively correlated with cyst size (R = 0.18, p = 0.020). In the subgroup of 14 perigestational MCN patients 29 % had HG-MCN and 71 % experienced cyst growth during gestation with an average growth of 55.1 ± 18 mm. CONCLUSIONS: Perigestational MCN are associated to increased cyst diameter, and in the subset of patients affected by MCN during gestation a high rate of growth was observed. Patients with a MCN and pregnancy desire should undergo multidisciplinary counselling.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".