The Western snip, stitch, and tug hydrocelectomy: How I do it
Bibliographic record
Abstract
INTRODUCTION: Adult idiopathic hydrocele is a common benign disorder that merits surgical correction when symptomatic. The most popular techniques for repair are plication (Lord's procedure) or excision and eversion of the tunica vaginalis (Jaboulay procedure). Established complications from these traditional repairs include hematoma, recurrence, and infection. These procedures are performed through a scrotal incision. We describe a novel technique of hydrocele repair with gubernaculum preservation through a subinguinal incision. METHODS: The novel technique is described in detail. A retrospective review was performed of those patients treated by a single surgeon with the subinguinal technique. Demographic information, indication for treatment, success rate, and details regarding complications were collected. RESULTS: We term the technique the "Snip, Stitch & Tug" repair. Through a small subinguinal incision, the tunica is everted posterior to the spermatic cord and testis without resection of the hydrocele sac or division of the gubernaculum. Twelve patients with postoperative followup were identified. Eleven patients (92%) treated with the novel technique were cured. There was only one complication (superficial wound infection) recorded after this technique. CONCLUSIONS: Idiopathic hydrocele repair with gubernaculum preservation can be easily and safely performed through a small subinguinal incision.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".