Tunica albuginea overlapping: a novel technique for the treatment of erectile dysfunction
Bibliographic record
Abstract
Tunica albuginea (TA) in venogenic erectile dysfunction (VED) was found subluxated and flabby because of degeneration and atrophy of its collagen fibres. This had apparently led to derangement of TA veno-occlusive mechanism. We investigated the hypothesis that overlapping of the subluxated and flabby TA would achieve a competent veno-occlusive mechanism during erection. Tunical overlapping was performed in nine VED patients (age 35.6 +/- 1.6 years). Intracorporal pressure (ICP) was measured pre- and postoperatively. After penile degloving, TA on lateral penile aspect was divided along whole length of corpus cavernosum (CC) and tunical double-breasting for 1-1 1/2 cm was performed. A biopsy was taken from TA and stained with haematoxylin and eosin and Masson's trichrome. Clinical efficiency of the operation was evaluated after 6 months. ICP increased (P < 0.01) postoperatively in the nine patients. The increase was maintained during follow-up period in eight patients and decreased to preoperative level in one. Six months after operation, the eight patients had significantly (P < 0.01) improved scores for the erectile function domain over the preoperative scores. Microscopic examination of TA biopsies showed atrophy of the collagen fibres. Tunical overlapping aims at correction of TA flabbiness, corporal tissue support and improving of veno-occlusive mechanism.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".