Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of the review is to summarize recent developments in time-lapse technologies and early embryo morphokinetics and to discuss their impact on current clinical outcomes. RECENT FINDINGS: Contemporary embryo culture and selection methodologies that are based on classical morphology are clearly limited in providing the most suitable embryo for a successful pregnancy. Noninvasive observation of embryo development by capturing the images with a time-lapse device has recently been proposed to be a better method of embryo viability assessment. Such methodologies have been shown to increase the quality and the quantity of information on the viability without disturbing the culture conditions. SUMMARY: Commercial availability of different time-lapse devices for human embryos facilitated the use of morphokinetics as an additional tool in human embryo selection. The application of such technologies has already shown positive results on clinical outcome by increasing our scope of traditional embryo selection, leading to higher implantation and clinical pregnancy rates. Additional benefit can come from the less-disturbed incubation environment that is created by all-in-one incubators. Such devices can also be very important research tools in order to observe and analyze the effect of different patient-specific or clinical conditions on embryo development parameters that are not available through classical embryo scoring.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".