Mini‐incision microdissection testicular sperm extraction: a useful technique for men with cryptozoospermia
Bibliographic record
Abstract
Microdissection testicular sperm extraction (micro-TESE) was developed to minimize the testicular injury associated with multiple open TESEs. We sought to evaluate a mini-incision micro-TESE in men with cryptozoospermia and non-obstructive azoospermia (NOA). We conducted a retrospective study of 26 consecutive men with NOA and cryptozoospermia who underwent a primary (first) micro-TESE between March 2015 and August 2015. Final assessment of sperm recovery (reported on the day of intra-cytoplasmic sperm injection (ICSI)) was recorded as (i) successful (available spermatozoa for ICSI) or (ii) unsuccessful (no spermatozoa for ICSI). The decision to perform a mini-incision micro-TESE (with limited unilateral micro-dissection) or standard/extensive (with unilateral or bilateral micro-dissection) was guided by the intra-operative identification of sperm recovery (≥5 spermatozoa) from the first testicle. Overall, sperm recovery was successful in 77% (20/26) of the men. In 37% of the men (8/26), the mini-incision micro-TESE was successful (positive sperm recovery). The remaining 18 men required a standard (extensive) microdissection: 61% (11/18) underwent a unilateral and 39% (7/18) a bilateral micro-TESE. We found that 90% (9/10) of the men with cryptozoospermia and 63% (10/16) of the men with NOA underwent a unilateral (mini or standard micro-TESE). The mini-incision micro-TESE allowed for successful sperm recovery in 60% (6/10) of the men with cryptozoospermia and 13% (2/16) of the men with NOA. The data demonstrate that a mini-incision micro-TESE together with rapid intra-operative assessment and identification of spermatozoa recovery can be useful in men undergoing microTESE, particularly, men with cryptozoospermia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".