Bibliographic record
Abstract
Advanced assisted reproductive technologies such as in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) are established treatment for severe male-factor infertility. The risk of transmitting existing genetic abnormalities to offspring through assisted reproduction has been a particular concern in male infertility cases due to Y-chromosome microdeletion, congenital bilateral absence of the vas deferens and Klinefelter’s syndrome, because these conditions generally required ICSI to achieve pregnancy. In addition, earlier studies raised the concerns of increased spontaneous abortion rate and chromosomal abnormalities with IVF and ICSI. Recently, well designed, large scale, population based studies concluded that assisted reproductive technology accounts for a more than a two-fold increase in the risk of low birth weight and major birth defects. Taken together, the bulk of the literature on the genetic risks of assisted reproduction highlights the importance of adequate pretreatment genetic evaluation and counselling. Furthermore, it is important to have proper infertility evaluation to identify and treat reversible causes of male-factor infertility that would allow couples to conceive naturally or opt for less invasive assisted reproductive technology.
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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".