Current Controversies in Prenatal Diagnosis—Conference Debate 2024: All Fetuses Undergoing Fetal Therapy Should Have Exome Sequencing
Bibliographic record
Abstract
This manuscript summarises the debate held at the 2024 annual meeting of The International Society for Prenatal Diagnosis (ISPD). Experts discussed whether all fetuses undergoing fetal therapy should undergo exome sequencing. Arguments in favor included that, with increasing experience and better clinical availability, exome sequencing can yield valuable diagnostic and prognostic information beyond what is available from karyotyping and microarray. This additional information is often helpful in counseling parents and provides a better understanding of fetal conditions, allowing for personalised medicine and supporting advancements in disease-focused fetal therapies. On the contrary, however, significant concerns regarding availability and health equity were raised. Moreover, potential delays in care incurred by exome sequencing may negatively affect outcomes of fetal intervention. Finally, as the information gathered from genetic testing may or may not affect pregnancy management decisions beyond termination of pregnancy, many families may choose not to undertake testing. The arguments of both debaters document current controversies in exome sequencing and genetic testing in general. This was also reflected in a divided audience vote at the end of the debate.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".