Fits in Pregnant Women with Previously Controlled Epilepsy: A Cross Sectional Study
Bibliographic record
Abstract
Background and Aim: Pregnant women with epilepsy require stability in maternal fits and antiepileptic drugs have potentially adverse effects in developing fetuses. Hypoxemia and blunt trauma cause convulsive fits which are treacherous to both maternal and fetus. Limited data available regarding frequency of fits in pregnant women with previously controlled epilepsy. The present study was carried out to evaluate the frequency of fits in pregnant women with previously controlled epilepsy. Methodology: This cross-sectional study was conducted on 70 pregnant women with a previous history of epilepsy in the Department of Obstetrics and Gynecology, Ayub Teaching Hospital, Abbottabad and Holy Family Hospital, Rawalpindifrom January 2021 to December 2021. The incidence of Fits during pregnancy was compared through a peripartum period (epoch 1) and during the postpartum period (epoch 2). Non-pregnant epilepsy women were enrolled as controls and were followed for 18 months period. A higher frequency of Fits was the prime outcome in epoch 1 compared to epoch 2. Administrated drugs such as antiepileptic drugs doses were compared. Results: A total of 70 pregnant and 60 controls women with epilepsy were enrolled. Out of 70, about 60 had a history of previous fits or Fits. The prevalence of fits was significantly higher during epoch 1 whereas in epoch 2, about 21% had Fits and 23% in control had Fits (Odd ratio: 0.89; 95% CI: 0.49-1.57). Of those 60, 22 (36.7%) of the pregnant subjects recruited had a history of eclampsia-related fits. An antiepileptic drug dose during pregnancy was changed in 69% of pregnant women and 29% in control (odd ratios; 6.41; 95% CI, 3.79-10.63). Conclusion: Our study found that the percentage of epilepsy diagnosed women had a higher frequency of fits during pregnancy compared to the postpartum period was comparable to the control group (non-pregnant) women. During comparable time periods, pregnant women experienced more changes in antiepileptic drug doses than non-pregnant women. Keywords: Fits, epilepsy, pregnancy
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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 | 0.002 |
| 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".