Sterile water injection for labour pain: a systematic review and meta‐analysis of randomised controlled trials
Bibliographic record
Abstract
BACKGROUND: Up to one-third of labouring women will experience painful 'back labour'. Sterile water injected lateral to the lumbosacral spine is a simple and well-researched approach to this pain. OBJECTIVE: To determine if sterile water injection for low back pain compared to placebo or alternative therapy increased or decreased the rate of Caesarean section. SEARCH STRATEGY: We performed a literature search with no language restriction in four databases: the Cochrane library, EMBASE (1980-2009), Ovid Medline (1950-2009) and CINAHL (1982-2009). SELECTION CRITERIA: We included all randomised controlled trials (RCTs) of sterile water injection for labour pain that included outcomes of interest and original data. DATA COLLECTION AND ANALYSIS: We compared Caesarean section rates among women who received sterile water injection in labour with those who received either placebo treatment or another non-pharmacological treatment modality. Other outcomes included pain scores, use of regional analgesia and women's assessment of treatment. We used Revman 5 for the meta-analysis. Data were entered by one reviewer and independently cross-checked. Pooled outcomes were reported as Relative Risk (RR) or Weighted Mean Difference using Mantel-Haenszel fixed-effects model except when the I2 value >50% indicated significant heterogeneity in which case random-effects model was used. MAIN RESULTS: We included eight RCTs. The Caesarean section rate was 4.6% in the sterile water injection group and 9.9% in the comparison group (n = 828) (RR 0.51, 95% CI: 0.30, 0.87). CONCLUSION: We believe that a large RCT should be mounted to validate our findings regarding the impact of sterile water injections on mode of delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.032 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".