Prevention of post‐dural puncture headache in parturients: a systematic review and meta‐analysis
Bibliographic record
Abstract
Post-dural puncture headaches (PDPHs) present an important clinical problem. We assessed methods to decrease accidental dural punctures (ADPs) and interventions to reduce PDPH following ADP. Multiple electronic databases were searched for randomised clinical trials (RCTs) of parturients having labour epidurals, in which the studied intervention could plausibly affect ADP or PDPH, and the incidence of at least one of these was recorded. Forty RCTs (n = 11,536 epidural insertions) were included, studying combined spinal-epidurals (CSEs), loss of resistance medium, prophylactic epidural blood patches, needle bevel orientation, ultrasound-guided insertion, epidural morphine, Special Sprotte needles, acoustic-guided insertion, administration of cosyntropin, and continuous spinal analgesia. The RCTs for CSE, loss of resistance medium, and prophylactic epidural blood patches were meta-analysed. Five methods reduced PDPH: prophylactic epidural blood patch {four trials, median quality score = 2, risk difference = -0.48 [95% confidence interval (CI): -0.88 to -0.086]}, lateral positioning of the epidural needle bevel upon insertion (one trial, quality score = 1), Special Sprotte needles [one trial, quality score = 5, risk difference = -0.44 (95% CI: -0.67 to -0.21)], epidural morphine [one trial, quality score = 4, risk difference = -0.36 (95% CI -0.59 to -0.13)], and cosyntropin [one trial, quality score = 5, risk difference = -0.36 (95% CI -0.55 to -0.16)]. Several methods potentially reduce PDPH. Special Sprotte needles, epidural morphine, and cosyntropin are thus far each supported by a single, albeit good quality trial. Prophylactic blood patches are supported by three trials, but these had flawed methodology. Mostly, trials were of limited quality, and further well-conducted, large studies are needed.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".