Successful Treatment of Genitofemoral Neuralgia Using Ultrasound Guided Injection: A Case Report and Short Review of Literature
Bibliographic record
Abstract
A young male patient developed chronic, severe, and disabling right sided groin pain following resection of his left testicular cancer. Since there is considerable overlap, ultrasound guided, selective diagnostic nerve blocks were done for ilioinguinal, iliohypogastric, and genitofemoral nerves, to determine the involved nerve territory. It was revealed that genitofemoral neuralgia was the likely cause. As a therapeutic procedure, it was injected with local anesthetic and steroid using ultrasound guidance. The initial injection led to pain relief of 3 months. Subsequent blocks reinforced the existing analgesia and were sufficient to allow for maintenance with the use of analgesic medications. This case report describes the successful use of diagnostic selective nerve blocks for the assessment of groin pain, subsequent to which an ultrasound guided therapeutic injection of genitofemoral nerve led to long term pain relief. As a therapeutic procedure, genitofemoral nerve block is done in patients with genitofemoral neuralgia. Ultrasound allows for controlled administration and greatly enhances the technical ability to perform precise localization and injection. There are very few case reports of such a treatment in the published literature. Apart from the case report, we also highlight the relevant anatomy and a brief review of genitofemoral neuralgia and its treatment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".