Embryonic Causes of Implantation Failure: A Systematic Review and Procedures To Optimize Successful Embryo Transfer
Bibliographic record
Abstract
and meta-analysis were reported in accordance with PRISMA guidelines. Quality assessment / Risk of bias analysisThe risk of bias in individual studies was evaluated using appropriate tools (e.g., Cochrane Risk of Bias tool for randomized controlled trials, Newcastle-Ottawa Scale for observational studies). Strategy of data synthesisMeta-analysis , Publication bias was assessed using funnel plots and statistical tests (e.g., Egger's test) if a sufficient number of studies were included in the meta-analysis.Subgroup analysis Subgroup analyses were conducted to explore potential sources of heterogeneity, such as study design, patient characteristics, or methodological differencesNo further sub groups were analyzed. Sensitivity analysisSensitivity analyses were performed to assess the robustness of the results by excluding studies with a high risk of bias or by e x p l o r i n g t h e i m p a c t o f s p e c i fi c s t u d y characteristics on the overall findings. Language restriction English. Country(ies) involved Italy.Keywords "in vitro fertilization," "assisted re p ro d u c t i v e t e c h n i q u e s , " " re p ro d u c t i v e t e c h n i q u e s , a s s i s t e d , " " I V F, " " e m b r y o implantation," "implantation failure," "embryo nidation," "endometrial receptivity," "endometrial decidualization".
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 | 0.033 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".