Pregnancy Outcomes in Women with Multiple Sclerosis Who Had Exposure to Ocrelizumab: a Systematic Review of the Literature.
Bibliographic record
Abstract
OBJECTIVE: To estimate the pooled prevalence of pregnancy-related issues in women who were exposed to Ocrelizumab before or during pregnancy. METHODS: PubMed, Scopus, EMBASE, Web of Science, Google Scholar, references and conference abstracts were comprehensively searched by two independent researchers. The search was conducted on 1 June 2023. RESULTS: A literature search revealed 320 records, of which 44 full-texts were evaluated and only five studies remained for the systematic review, among which one study was conducted in Australia, one in the United States, one in Canada and one in the United Kingdom. All eligible studies were conducted between 2017 and 2022. They included a number of patients ranging from 12 to 608, and totalized 1 305 participants and 1 306 pregnancies. The pooled prevalence of term delivery was 46% (95% CI 31-61%) (I²=94.5%, P<0.001). The pooled prevalence of abortion was 9% (95% CI 4-14%) (I²=80%, P<0.001). Three studies reported on the number of ectopic pregnancies. The pooled prevalence of ectopic pregnancies was 5% (95% CI 2-4%) (I²=60%, P<0.001). The pooled prevalence of major congenital malformations was 2% (95% CI 1-3%) (I²=0). CONCLUSIONS: The results of this systematic review show that exposure to Ocrelizumab during conception is not associated with a higher frequency of preterm deliveries or major malformations.
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 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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".