Low Back Pain in Pregnancy: Investigations, Management, and Role of Neuraxial Analgesia and Anaesthesia: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is commonly experienced during pregnancy and is often poorly managed. There is much ambiguity in diagnostic work-up, appropriate management and decision-making regarding the use of neuraxial analgesia and anaesthesia during labour and delivery in these patients. This systematic review summarises the evidence regarding investigations, management strategies and considerations around performing neuraxial blocks for pregnant women with LBP. METHODS: We searched 3 databases and reviewed literature concerning LBP in pregnancy with regards to diagnostic modalities, management strategies and use of neuraxial techniques for facilitating labour and delivery. RESULTS: In all, we included 78 studies in this review, with 32 studies concerning diagnostic investigations, 56 studies involving management strategies, and 4 studies regarding the use of neuraxial techniques for labour and delivery. SUMMARY: MRI is the safest investigative modality for LBP in pregnancy. Antenatal educational programmes, exercise and steroid injections into the epidural space or sacroiliac joints may help with pain management. Worsening neurological deficits, vertebral fractures and tumours may need surgical management. There is limited evidence on challenges of performing neuraxial blocks in the peripartum period for analgesia and anaesthesia, but there is a potential for increased risk of neurological complications in parturients with pre-existing neurological deficits.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".