Use of testicular sperm for ICSI in oligozoospermic couples: how far should we go?
Bibliographic record
Abstract
In 1992 and subsequently, several reports indicated that ICSI was a successful technique to achieve clinical pregnancy and live birth using spermatozoa with severely impaired characteristics. The initial optimism over the ability of ICSI to overcome significant sperm abnormalities was later tempered by the findings of more recent publications suggesting that some sperm deficits may not be as effectively treated with ICSI. In search for effective treatment for couples with severe male factor, a number of small retrospective and prospective studies have reported high pregnancy and live birth rates using testicular sperm for men with necrozoospermia, cryptozoospermia and oligozoospermia with or without elevated sperm DNA damage. Although the data suggest that there may be some benefit in performing testicular sperm retrieval (TSR)-ICSI in select groups of non-azoospermic infertile men, there are potential risks involved with TSR. Clinicians should balance these risks prior to the recommendation of TSR-ICSI on the result of a semen analysis or sperm DNA test alone. Careful evaluation and management of male factor infertility is important. The use of TSR-ICSI in the absence of specific sperm DNA defects is still experimental.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".