Low Back Pain in Pregnancy–Investigations, Management, and the Role of Neuraxial Analgesia: A Narrative Review [2L]
Bibliographic record
Abstract
INTRODUCTION: Low back pain (LBP) is common in pregnancy affecting approximately 50% of pregnant women. Yet, there is much ambiguity with regard to the diagnostic work-up and management of pregnant women with LBP. This narrative review summarizes evidence surrounding investigations, management strategies and the considerations around performing neuraxial blocks for pregnant women with LBP to inform clinical practice. METHODS: MEDLINE, MEDLINE In-Process and Embase were searched from inception to November 2015. The search was limited to humans and restricted to the English language. All studies from case reports to randomized controlled trials describing diagnostic modalities, management strategies and use of neuraxial techniques during labor and delivery in pregnant women with LBP were identified. RESULTS: A total of 6803 records were identified through database-searching and 60 through citation-tracking, of which 77 studies were included. Of these, 32 described the use of diagnostic modalities, 56 described management strategies, and three reported the use of neuraxial techniques for labor and delivery. Magnetic resonance imaging is the safest diagnostic modality for LBP in pregnancy. Antenatal educational programs, exercise and steroid injections into the epidural space or sacroiliac joints may help with pain management. Worsening neurological deficits, vertebral fractures, and tumors may need surgical management. There is limited evidence on challenges of performing neuraxial blocks in the peripartum period but there is a potential for increased risk of neurological complications in parturients with pre-existing neurological deficits. CONCLUSION: This review summarizes the available evidence and provides a clinical algorithm for the diagnosis and management of pregnant women with LBP.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".