Dural Tissue Trauma and Cerebrospinal Fluid Leak after Epidural Needle Puncture
Bibliographic record
Abstract
BACKGROUND: The effects of epidural needle design, angle, and bevel orientation on cerebrospinal fluid leak after puncture have not been reported. The impact of these factors on leak rate was examined using a dural sac model. Dural trauma was examined using scanning electron microscopy. METHODS: Human cadaveric dura, mounted on a cylindrical model, was punctured with epidural needles using a micromanipulator. Tissue was punctured at 15 cm H2O (left lateral decubitus) system pressure, and leak was measured at 25 cm H2O (semisitting) pressure. Leak rates and trauma were compared for the following: (1) six different epidural needles at 90 degrees, bevel parallel to the dural long axis; (2) 18-gauge Tuohy and 18-gauge Special Sprotte epidural needles, 30 degrees versus 90 degrees; (3) 18-gauge Tuohy, bevel perpendicular versus parallel to the dural long axis. RESULTS: With the 90 degrees puncture, bevel parallel, the greatest leak occurred with a 17-gauge Hustead (516 +/- 319 ml/15 min), and the smallest leak occurred with a 20-gauge Tuohy (100 +/- 112 ml/15 min; P = 0.0018). A 20-gauge Tuohy puncture led to statistically significant reductions in leak (P value range, 0.0001-0.0024) compared with all needles except the Special Sprotte. With the 30 degrees versus 90 degrees angle, 30 degrees punctures with an 18-gauge Tuohy produced nonstatistically significant leak reductions compared with the 18-gauge Tuohy at 90 degrees. The puncture angle made no difference for the Special Sprotte. Nonsignificant reductions were found for the Special Sprotte compared with the Tuohy. With the 18-gauge Tuohy bevel orientation, perpendicular orientation produced nonstatistically significant reductions in leak compared with parallel orientation. CONCLUSIONS: Cerebrospinal fluid leak after puncture was influenced most by epidural needle gauge. Leak rate was significantly less for the 20-gauge Tuohy needle.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".