Evaluating Treatment of Antiphospholipid Syndrome during Pregnancy with Knowledge Synthesis Methods
Bibliographic record
Abstract
Background: Pregnancies complicated by antiphospholipid syndrome (APS) are at an increased risk of adverse pregnancy outcomes, including recurrent miscarriage, stillbirths, placental insufficiencies and maternal thrombosis. APS during pregnancy remains a challenge, since these complications persist, even when treated with standard regimens. Objective: To evaluate the published evidence on key information of medications to address their effectiveness and safety for pregnant women with APS. Methods: Given variability of the published information on pregnancy uses of APS medications, I approached this research in a systematic manner combining multiple methodologies. My first study was a systematic review and meta-analysis of the pregnancy safety of statins, which are being repurposed and have emerged as a new group of medications to manage APS. Next, I explored the use of vitamin k antagonists (VKAs) in pregnant women with APL using a scoping review approach, which are discontinued during the first trimester due to established teratogenicity. Finally, a systematic review was conducted to assess the pregnancy associated pharmacokinetic changes of three groups of medications used for APS (1. low-dose aspirin; 2. unfractionated heparin and low-molecular weight heparin; and 3. Chloroquine and hydroxychloroquine). Results and Conclusion: There was no significant increase in malformations in the statin exposed pregnancies. However, there was a small but significant increase in spontaneous abortions. It is not clear if this is due to the medication or underlying disease of the exposed group. There was an apparent geographical variation in the utilization of VKA and a lack of systematic investigation. The resultant information gap is wide relative to its exploration in pregnant women with mechanical heart valves. Although heterogeneity of PK analysis methods in the literature was striking, apparent clearance of these medications was consistently increased during pregnancy, likely based on the physiological changes in pregnancy. While doses of heparins are often increased during pregnancy, the other 2 medication groups are not fully investigated for the need of dose modification during pregnancy. Moreover, I confirmed PK evidence is largely lacking for women in general, let alone pregnant women with APS, creating a huge knowledge gap and therapeutic void in this 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 | 0.028 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".