Preconception counselling for women with acromegaly: More questions than answers
Bibliographic record
Abstract
BACKGROUND AND AIMS: Approximately 174 pregnancies in acromegaly have been reported. Our objectives were to identify the key challenges of preconception counselling in this population. METHODS: Case series of three acromegalic women with desire for pregnancy. Issues were identified from chart review and discussion with attending physicians. Literature review of acromegaly and pregnancy was conducted. RESULTS: Important issues identified included: impact of acromegaly on fertility, management of acromegaly in the peripartum period, screening for associated conditions, risk of progression of acromegaly/tumour growth during pregnancy, impact of acromegaly on pregnancy outcomes, surveillance during pregnancy, method of delivery and impact on neonatal outcomes and breastfeeding. CONCLUSIONS: Pregnancy can be safely achieved in patients with acromegaly. There is little evidence to guide recommendations around conception and pregnancy surveillance. Patients can be reassured that in most situations, pregnancy proceeds without complication and that medical treatment can be used during pregnancy if necessary.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".