Accuracy of Ultrasound-Guided Pudendal Nerve Block in the Ischial Spine and Alcock’s Canal Levels: A Cadaveric Study
Bibliographic record
Abstract
BACKGROUND: Blockade of the pudendal nerve (PN) using ultrasound (US) guidance has been described at the levels of the ischial spine and Alcock's canal. However, no study has been conducted to compare anatomical accuracy between different approaches in targeting the PN. OBJECTIVE: To investigate the accuracy of US-guided injection of the PN at the ischial spine and Alcock's canal levels. This study also compared the accuracy of the infiltrations by three sonographers with different levels of experience. SUBJECTS: Eight Thiel-embalmed cadavers (16 hemipelvises). METHODS: Three physiatrists trained in musculoskeletal US imaging with 12 years, five years, and one year of experience performed the injections. Each injected a 0.1-mL bolus of colored dye in both hemipelvises of each cadaver at the ischial spine and Alcock's canal levels under US guidance. Each cadaver received three injections per hemipelvis. The accuracy of the injection was determined following hemipelvis dissection by an anatomist. RESULTS: The injections were accurate 33 times out of the total 42 attempts, resulting in 78% accuracy. Sixteen out of 21 injections at the ischial spine level were on target (76% accuracy), while the approach at Alcock's canal level yielded 17 successful injections (81% accuracy). The difference between the approaches was not statistically significant. There was also no significant difference in accuracy between the operators. CONCLUSIONS: US-guided injection of the PN can be performed accurately at both the ischial spine and Alcock's canal levels. The difference between the approaches was not statistically significant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".