Therapy with both magnesium sulfate and nifedipine does not increase the risk of serious magnesium-related maternal side effects in women with preeclampsia
Bibliographic record
Abstract
Objective: Does the use of nifedipine and magnesium sulfate together increase serious magnesium-related effects? Study design: This was a retrospective chart review of women who were admitted to BC Women' s Hospital and Health Centre (1997-2001) and were given intravenous magnesium sulfate for preeclampsia. Serious magnesium-related effects were compared among 162 cases who received magnesium sulfate and contemporaneous nifedipine and 215 control subjects who received magnesium sulfate and either another antihypertensive (n = 32 women) or no antihypertensive (n = 183 women) medication. Chi-squared test, Fisher' s exact test, or the Student t test was used for data comparison between cases and each control group. A probability value of .05 was considered statistically significant. Results: The cases had more severe preeclampsia and a longer magnesium sulfate infusion. However, cases had no excess of neuromuscular weakness (53.1% ) versus control subjects who received antihypertensive medication (53.1% ; P = .99) or control subjects who received no antihypertensive medication (44.8% ; P = .13) or other serious magnesium-related effects. Cases versus control subjects who received antihypertensive medication had less neuromuscular blockade (odds ratio, 0.04; 95% CI, 0.002-0.80). Cases versus control subjects who received no antihypertensive medication had less maternal hypotension (41.4% vs 53.0% ; P = .04). Conclusion: The use of nifedipine and magnesium sulfate together does not increase the risk of serious magnesium-related effects.
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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 | 0.009 |
| 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".