Reperfusion Therapy for Acute Stroke in Pregnant and Post-Partum Women: A Canadian Survey
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: A Canadian Stroke Best Practices consensus statement on Acute Stroke Management during pregnancy was published in 2018. The state of individual practice, however, is unknown. METHODS: A survey on treatment of acute stroke in pregnant and post-partum women was distributed via the Canadian Stroke Consortium email list. Descriptive statistics (frequencies and proportions) were calculated for demographic and response variables and free-text responses were coded for thematic content. RESULTS: Thirty-five participants completed the survey; 12 had experience with intravenous tissue plasminogen activator (IV-tPA), endovascular therapy (EVT), or both in pregnant patients. None had treatment-related complications. The majority (92%) of those who had not yet encountered the issue in practice expressed some reservation about giving IV-tPA to an otherwise eligible pregnant woman. In a theoretical scenario where an otherwise eligible pregnant woman was a candidate for both IV-tPA and EVT, 58% of respondents would have opted for EVT alone. Amongst this cohort comprised mainly of stroke sub-specialists, more than a third had treated pregnant patients with reperfusion therapy. CONCLUSIONS: The reported safety experience with both IV-tPA and EVT was reassuring. Overall, there was a hesitancy towards use of IV-tPA in pregnancy that is discordant with the recent consensus statement. Possible barriers to uptake identified through thematic analysis were concerns regarding risks of bleeding in the pregnant patient, presence of EVT as a perceived alternative, and the need for express consent from the patient and family.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".