ESHRE Capri Workshop Group. Diagnosis and management of the infertile couple: missing information.
Bibliographic record
Abstract
Missing information about reproductive processes is an important barrier to conception and live birth among infertile couples. In vitro and genetic techniques have been informative, but not enough is known about potential defects in fertilization, implantation and early embryo development to define explicit infertility diagnoses and direct specific treatment. Study of gamete quality is needed because endocrine and ultrasound measurements in the female cannot determine oocyte quality and semen analysis in the male is a limited predictor of fertilizing ability. Also, more study would help in understanding the conditions in the Fallopian tube and elsewhere that foster fertilization and embryo development. Research in related subjects is also needed: after many years of enquiry, there exists little or no evidence to determine how and if endometriosis causes subfertility. In contrast, the recently described impact of smoking in males is large, clinically important and possibly reversible. Missing information limits the choice of specific treatment, puts a ceiling on the overall prognosis, increases the likelihood of persistent infertility and forms a barrier to understanding the broader meanings of success in the management of infertility.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".