Neuraxial analgesia in pregnant individuals with bleeding disorders: a retrospective descriptive study of obstetric anesthesia practices and outcomes
Bibliographic record
Abstract
Background: Neuraxial analgesia is an effective and advantageous technique for managing labor pain. Yet, clinicians often hesitate to offer neuraxial analgesia to parturients with inherited bleeding disorders because of fear of epidural or spinal hematoma and the absence of high-quality evidence to guide decision-making in practice. Objectives: To describe obstetric analgesia practices and outcomes in pregnant individuals with bleeding disorders managed at a tertiary care center. Methods: We performed a retrospective descriptive study of all deliveries (January 2010-July 2021) managed by the Ottawa Regional Bleeding Disorders Program. Patient, laboratory, peripartum anesthetic data, and antenatal anesthesia and hematology recommendations were collected from electronic health records and summarized descriptively. Results: Eighty-two deliveries occurred in 56 pregnant individuals (median age, 31 years). The most common disorders were type 1 von Willebrand disease (34.1%) and hemophilia A (29.3%). Neuraxial analgesia was used in 59 of 82 deliveries (72.0%). Third-trimester hemostatic profiles met predefined "adequate" thresholds in 54 of 79 evaluable deliveries (68.4%). Of the 25 deliveries below target, 13 still received neuraxial analgesia, and 10 of those received factor replacement beforehand. Antenatal recommendations from anesthesiology and hematology were documented in 47 cases, with agreement in 45 (95.7%). No epidural or spinal hematomas occurred. Minor morbidity included 1 postdural puncture headache (1.7%) and 2 cases of localized back pain (3.4%). Conclusion: With multidisciplinary planning and evidence-aligned laboratory targets, neuraxial analgesia was offered safely to most pregnant individuals with bleeding disorders within our cohort. Larger multicenter studies and registry initiatives are warranted to refine factor-specific thresholds and standardize care pathways for this patient population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".