Sperm DNA fragmentation index and high DNA stainability do not influence pregnancy success after intracytoplasmic sperm injection
Bibliographic record
Abstract
OBJECTIVE: To evaluate the ability of sperm DNA fragmentation index (DFI%) and high DNA stainability (HDS%) to influence the chance of achieving pregnancy in couples undergoing intracytoplasmic sperm injection (ICSI) cycles. DESIGN: A retrospective study evaluating couples that underwent an ICSI cycle between 2009 - 2018. SETTING: High-volume reproductive center. PATIENTS: Consecutive couples who underwent an ICSI cycle and had a semen analysis with subsequent DFI% and HDS% testing, evaluated by Sperm Chromatin Structure Assay (SCSA). INTERVENTIONS: Measurement of DFI% and HDS% prior to ICSI cycle. MAIN OUTCOME MEASURES: To determine whether DFI% or HDS% of sperm was predictive of the number of ICSI cycles until the first clinical intrauterine pregnancy. RESULTS: A total of 550 couples who underwent 1050 ICSI cycles were analyzed. Of those, a total of 330 couples achieved pregnancy. As expected, in couples that achieved pregnancy, females were younger (33.7 ± 3.6 years vs 35.3 ± 3.4 years; p < 0.001) and underwent fewer cycles (2 [1-2] vs 2 [1-3]; p =0.001). Importantly, the DFI% and HDS% were similar between couples who achieved pregnancy (DFI% = 12.9 [8-20]; HDS% = 9.3 [6.1-14.6]) and couples who did not (DFI% =12.2 [7.1-20.2]; HDS% = 9.1 [6.7-14]). A multivariable-adjusted analysis evaluating female age at the first cycle was negatively associated with pregnancy (OR = 0.827, 95% CI: 0.778 - 0.879; p < 0.001). CONCLUSIONS: Neither DFI nor HDS at baseline influence the chances of a couple to achieve pregnancy after ICSI. Increased female age and couples who underwent more ICSI cycles were associated with lower chances of achieving pregnancy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".