Descriptive analysis and outcomes of PitNETs treated surgically during pregnancy and postpartum
Bibliographic record
Abstract
OBJECTIVE: Data on pituitary neuroendocrine tumours (PitNETs) surgically treated during pregnancy are limited, and no studies have compared these cases to those treated in non-pregnant women. This study aimed to describe the clinical, radiological, and histological profiles of patients treated surgically for PitNETs during pregnancy and evaluate long-term prognosis. DESIGN: This study was multicentric, observational, and retrospective. METHODS: We included 10 patients from 5 university hospitals who underwent surgical treatment for PitNETs during pregnancy or within 12 months postpartum, along with 30 matched non-pregnant controls treated surgically for PitNETs. Clinical and histological data, as well as progression-free survival without additional treatment, were compared between pregnant and non-pregnant patients. RESULTS AND CONCLUSIONS: Among the 10 PitNETs, 4 were corticotropic, 2 gonadotropic, 2 lactotropic, and 2 somatotropic. The primary surgical indication (tumour syndrome with or without failure of medical treatment) was similar between the two groups: 7/10 vs 19/30 (P = 1.00). There was no statistically significant difference in volume (P = .072) or radiological invasion markers (optic chiasm compression, P = .059, and cavernous sinus invasion, P = .274). However, PitNETs in pregnant women showed higher mitotic activity (P = .038) and were more frequently classified as grade 2b (Trouillas clinicopathological classification; P = .049). The need for second-line treatment was also more frequent (P = .005). PitNETs requiring surgical treatment during pregnancy are characterized by increased proliferative activity and progression after surgery. Despite this, the long-term prognosis remains favourable. These results need confirmation in a larger study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".