Myasthenia in pregnancy: best practice guidelines from a UK multispecialty working group
Bibliographic record
Abstract
A national U.K. workshop to discuss practical clinical management issues related to pregnancy in women with myasthenia gravis was held in May 2011. The purpose was to develop recommendations to guide general neurologists and obstetricians and facilitate best practice before, during and after pregnancy. The main conclusions were (1) planning should be instituted well in advance of any potential pregnancy to allow time for myasthenic status and drug optimisation; (2) multidisciplinary liaison through the involvement of relevant specialists should occur throughout pregnancy, during delivery and in the neonatal period; (3) provided that their myasthenia is under good control before pregnancy, the majority of women can be reassured that it will remain stable throughout pregnancy and the postpartum months; (4) spontaneous vaginal delivery should be the aim and actively encouraged; (5) those with severe myasthenic weakness need careful, multidisciplinary management with prompt access to specialist advice and facilities; (6) newborn babies born to myasthenic mothers are at risk of transient myasthenic weakness, even if the mother's myasthenia is well-controlled, and should have rapid access to neonatal high-dependency support.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".