2-9THE USE OF IBUPROFEN AS POSTPARTUM ANALGESIC IN WOMEN WITH HYPERTENSIVE DISORDER OF PREGNANCY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Postpartum women are commonly prescribed with non-steroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen, which is effective for pain management. However, NSAIDs were known to induced hypertension. Thus, the use of NSAIDs in postpartum women with hypertension is still controversial due to a lack of evidence. This study aims to identify the use of ibuprofen as postpartum analgesics in women with hypertensive disorder of pregnancy. Methods: A systematic literature search was conducted using specific keywords with medical subheading (MeSH) terms on PubMed, EBSCOhost, Cochrane, and ProQuest for primary studies published from January 2009 – December 2019. Two authors independently screened and assessed the potential studies using inclusion criteria. The quality of the randomized controlled trial studies was assessed with JADAD scale and Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of cohort studies. Result: After screening the literature, four randomized controlled trials (RCTs) and two retrospective cohort studies were identified, comprising of 895 total patients. All studies except one RCT showed that ibuprofen did not increase postpartum blood pressure in postpartum women with hypertensive disorders of pregnancy compared with women without the exposure of NSAIDs. Those who were receiving ibuprofen showed no difference in postpartum mean arterial pressure (MAP) compared to control. No significant adverse events were also found in postpartum women. Discussion: This review demonstrates that the use of ibuprofen may be beneficial and can be used for postpartum women with hypertension. However, further studies with a larger population are still needed to confirm these results.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".