Patients who failed to conceive following an in vitro fertilization cycle can be clustered into different failure causes using gene expression hierarchical analysis†
Bibliographic record
Abstract
The patient's response to an IVF stimulation protocol is highly variable and thus difficult to predict. When a cycle fails, there are often no apparent or obvious reasons to explain the failure. Having clues on what went wrong during stimulation could serve as a basis to improve and personalize the next protocol. This exploratory study aimed to investigate if it is possible to distinguish different failure causes or different follicular responses in a population of nonpregnant IVF patients. Using qRT-PCR, we analyzed a panel of genes indicative of different failure causes in patients who did not achieve pregnancy following an IVF cycle. For each patient, a pool of follicular cells from all aspirated follicles was used as a sample which gives a global picture of the patient's ovary and not a specific picture of each follicle. We performed hierarchical clustering analysis to split the patients according to the gene expression pattern. Hierarchical analysis showed that the population of nonpregnant IVF patients could be divided into three clusters. Gene expression was significantly different, and each cluster displayed a particular gene expression pattern. Follicular cells from patients in clusters 1, 2 and 3 displayed respectively a pattern of gene expression related to large incompetent follicles with a higher apoptosis (over matured), to follicles not ready to ovulate (under mature) and to an excess of inflammation with no visible symptoms. This study reinforces the idea that women often have different response to the same protocol and would benefit from more personalized treatments.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".