Management of prolactinomas during pregnancy – A survey of four Canadian provinces
Bibliographic record
Abstract
PURPOSE: The guidelines for management of prolactinomas during pregnancy are mostly based on retrospective evidence or expert opinion. We conducted a survey to assess the current trends in management of prolactinomas during pregnancy. METHODS: A case-based electronic questionnaire was sent in January 2011 to all practicing endocrinologists, in four Canadian provinces: Nova Scotia, New Brunswick, Prince Edward Island and British Columbia with three cases of varying severity; ranging from a microprolactinomas to a large macroprolactinomas compressing the optic chiasm. RESULT: There was a considerable diversity among endocrinologists with regards to monitoring and managing prolactinomas during pregnancy. In case of microprolactinomas, 94% of specialists would discontinue dopamine agonist (DA) therapy upon confirmation of pregnancy, 79% would discontinue serum prolactin measurement during pregnancy, and 94% would not perform routine pituitary imaging in the absence of new symptoms whereas 32% would perform regular formal visual field (VF) testing throughout pregnancy. In the case of macroprolactinomas, 65% chose to discontinue DA therapy upon confirmation of pregnancy, 30% would either perform regular MRI during pregnancy or, if serum prolactin was thought to be elevated out of proportion, with clinical judgment and 40% would not perform regular formal VF monitoring during pregnancy. In management of large macroprolactinomas, 82% elected to continue DA therapy whereas 18% chose surgical excision as the treatment of choice. Forty nine percent would perform regular MRI during pregnancy and 94% would perform regular formal VF monitoring during pregnancy. CONCLUSION: Among endocrinologists there is considerable diversity in management of prolactinomas during pregnancy, indicating a need for better consensus and clearer guidelines.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".