Tracking of oocyte dysmorphisms for ICSI patients may prove relevant to the outcome in subsequent patient cycles
Bibliographic record
Abstract
BACKGROUND: We determined whether oocyte dysmorphisms, especially repetition of specific dysmorphisms from cycle to cycle, had a prognostic impact on intracytoplasmic sperm injection (ICSI) outcome. METHODS: ICSI patients (n = 67) were grouped as follows: group 1 >50% phenotypically dysmorphic oocytes per cohort (cytoplasmic and extra-cytoplasmic dysmorphisms) with no repetition of a specific dysmorphism from cycle one to cycle two (36 cycles and 274 oocytes); group 2 >50% dysmorphic oocytes per cohort and repetition of the same dysmorphism from cycle one to cycle two (32 cycles and 313 oocytes); group 3 (control) <30% dysmorphic oocytes (33 cycles and 378 oocytes). RESULTS: In group 2 (repetitive), 47% of oocytes were observed to have organelle clustering versus 20.5% in group 1 and 17.3% in group 3 (P < 0.001). There was no difference between the groups in fertilization rates, cleavage rates or embryo quality. Embryos derived from normal oocytes were transferred in each group (57, 33 and 72% respectively). The clinical pregnancy and implantation rates in group 2 (3.1 and 1.7% respectively) were lower (P < 0.01, P = 0.005) than both group 1 (28 and 15% respectively) and group 3 (45.5 and 26.5% respectively). CONCLUSIONS: The low implantation rate in group 2, even though 33% of transferred embryos were derived from morphologically normal oocytes, suggests that repetitive organelle clustering may be associated with an underlying adverse factor affecting the entire follicular cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".