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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".