Use of testicular sperm in couples with SCSA-defined high sperm DNA fragmentation and failed intracytoplasmic sperm injection using ejaculated sperm
Bibliographic record
Abstract
Sperm DNA fragmentation (SDF) has been linked with male infertility, and previous studies suggest that SDF can have negative influence on pregnancy outcomes with assisted reproduction. We performed a retrospective review of consecutive couples with a high SDF level that had intracytoplasmic sperm injection (ICSI) using testicular sperm (T-ICSI). We compared the T-ICSI outcomes to that of two control groups: 87 couples with failed first ICSI cycle and who had a second ICSI cycle using ejaculated sperm (Ej-ICSI), and 48 consecutive couples with high sperm chromatin structure assay (SCSA)-defined SDF (>15%) that underwent an ICSI cycle using ejaculated sperm after one or more failed ICSI cycles (Ej-ICSI-high SDF). The mean number of oocytes that were retrieved and the total number of embryos were not different among the three groups. The mean number of transferred embryos in the T-ICSI group was higher than the Ej-ICSI group but not significantly different than the Ej-ICSI-high SDF group (1.4, 1.2, and 1.3, respectively, P < 0.05). Clinical pregnancy rate in the T-ICSI group was not significantly different than the Ej-ICSI and Ej-ICSI-high SDF groups (48.6%, 48.2%, and 38.7%, respectively, P > 0.05). No significant difference was found in live birth rate when comparing T-ICSI to Ej-ICSI and Ej-ICSI-high SDF groups. The results suggest that pregnancy outcomes and live birth rates with T-ICSI are not significantly superior to Ej-ICSI in patients with an elevated SCSA-defined sperm DNA fragmentation and prior ICSI failure(s).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".