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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".