Counselling of a couple faced with a prenatal diagnosis of Klinefelter syndrome
Bibliographic record
Abstract
UNLABELLED: When a prenatal diagnosis of Klinefelter syndrome (KS) is made, a couple is faced with an unfamiliar and unexpected diagnosis. The aim of this article is to give clues to prenatal counselling in this situation. The information provided to couples facing a prenatal diagnosis of KS should ideally be based on longitudinal studies of unselected individuals, including those diagnosed prenatally. Indeed, there are several reasons to think that the phenotype of individuals diagnosed prenatally is globally less severe than in those diagnosed postnatally. Based on these studies, the evidence to be explained to couples to help them make an informed decision about the pregnancy is the following: except for rather tall height, generally normal appearance throughout life; increased risk of learning disabilities; spontaneous puberty, reduced testicular size, usual need for testosterone supplementation from adolescence onward; increased risk of gynecomastia; sexual orientation similar to the general male population; infertility, but with the possibility of having biological offspring with assisted reproductive techniques. In this article, we review the evidence about the phenotype of KS according to the circumstances of diagnosis and its use in counselling couples faced with a prenatal diagnosis of this common condition. CONCLUSION: Cohort studies including individuals with KS diagnosed prenatally are still lacking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".