The Use of Electrical Stimulation to Monitor Epidural Needle Advancement in a Porcine Model
Bibliographic record
Abstract
In Brief Muscle twitches elicited with electrical stimulation (ES) during epidural insertion may indicate epidural needle location. We examined the potential application of ES at 5 mA as a continuous method of monitoring the response to epidural needle advancement in a porcine model. Five 20-kg pigs were used in this study. A needle with a stimulating current of 5 mA was inserted at 20 separate levels in each pig. The needle was advanced until a muscle twitch was observed without loss-of-resistance (LOR). The needle position was then assessed using LOR. At the end of the experiment, an autopsy was performed to assess the spinal cord for injury. A total of 100 needle insertions were performed in the 5 pigs. The threshold current in the epidural space was 3.6 ± 0.6 mA. In 59 of the needle insertions, LOR was not obtained at the depth at which a muscle twitch was initially observed. However, after advancing these 59 needles another 1–2 mm, LOR was obtained. In the other 41 insertions, LOR was observed without further advancement of the needle. Autopsies indicated there were no dural punctures or spinal cord damage in any of the pigs. These observations suggest that ES can be used to signal that the epidural needle is in or approaching the epidural space. However, the high false positive predictive value (59%) makes it impractical and unreliable to detect the precise entry of a needle into the epidural space in pigs. IMPLICATIONS: Hypothetically, electrical stimulation may be able to be used to provide advanced warning that an epidural needle is approaching the epidural space. However, using 5 mA is an unreliable method to detect the precise point needle entry into the epidural space in pigs.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".