Bibliographic record
Abstract
1Women with multiple sclerosis (MS) want to enjoy life to the full, including giving birth to and raising children. MS, in itself, has little impact on the course of pregnancy. It is well established that disease activity is considerably reduced during the last two trimesters of pregnancy. 1 Breast-feeding has a neutral effect on MS course. We have reported that pregnancy may hasten transition to secondary progression. 2 Family planning for a woman with MS must consider the following: a 3–5% risk of MS transmission to the child, treatment cessation, a post-partum relapse rate of around 30%, resumption of disease-modifying therapies (DMT), and the possibility of a lessened capacity to take care of the child. The only demonstrated effects of standard DMTs on pregnancy outcomes are a slightly decreased child birth weight and length, and an increased rate of spontaneous abortions with interferons. 3 Uncertainty remains over some possible long-term effects of treatments on the child. Women are generally unwilling to take any DMT during pregnancy, so that treatment is almost always deferred. DMT interruption carries the risk of disease reactivation, so that it is now advised to continue DMTs until a pregnancy test is positive. There is still, though, a high percentage of unplanned pregnancies. Natalizumab (NZB) is highly effective in reducing clinical and magnetic resonance imaging (MRI) manifestations of MS. Its use as a 4-weekly IV infusion is convenient, and is well tolerated in the short term. Alpha4-integrin, which is the target of NZB, plays an active role in fertilization, implantation, placental and cardiac development. When antagonized with NZB in pregnant animals, severe birth defects have been reported. Risks associated with NZB exposures during pregnancy and potential adverse effects on the developing fetus have therefore been expected.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".