Verifying Accurate Placement of an Epidural Catheter Tip Using Electrical Stimulation
Bibliographic record
Abstract
To the Editor: We are pleased to read the article by Hayatsu et al. (1) in which they described how electrical stimulation could be used to verify proper placement of an epidural catheter in the epidural space. Evidently, the authors were not aware of our earlier observations on the same topic (2–10). In those clinical reports, we described the successful use of electrical epidural stimulation to confirm epidural catheter placement in the epidural space in both adult and pediatric patients. In our most recent article (3), we demonstrated the practicality of applying this test to guide the placement of a thoracic epidural catheter using the caudal approach in pediatric patients. In that article, we provided a simplified guide to interpret motor responses from electrical stimulation in the event of malplacement of epidural catheters (for instance, subarachnoid, subdural, or intravascular). From a technical point of view, Hayatsu et al. (1) reported a method using bipolar electrical stimulation that required a specialized catheter, which may limit its application. However, their specialized catheter can provide many other functions, including monitoring of spinal cord potentials, measurement of epidural pressure, and spinal cord stimulation. On the other hand, we described a technique using monopolar electrical stimulation that can be performed with one of the commonly available epidural catheters, which can be readily applied in routine practice. Ban C.H. Tsui, MSc, MD, FRCP(C) Brendan Finucane, MB MS, FRCPC (C)
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".