Beyond Isolated and Associated: A Novel Fetal MR Imaging–Based Scoring System Helps in the Prenatal Prognostication of Callosal Agenesis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although "corpus callosum agenesis" is an umbrella term for multiple entities, prenatal counseling is based reductively on the presence (associated) or absence (isolated) of additional abnormalities. Our aim was to test the applicability of a fetal MR neuroimaging score in a cohort of fetuses with prenatally diagnosed isolated corpus callosum agenesis and associated corpus callosum agenesis and correlate it with neurodevelopmental outcomes. MATERIALS AND METHODS: We performed a single-center retrospective analysis of a cohort of cases of consecutive corpus callosum agenesis collected between January 2011 and July 2019. Cases were scored by 2 raters, and interater agreement was calculated. Outcome was assessed by standardized testing (Bayley Scales of Infant and Toddler Development, Kaufman Assessment Battery for Children) or a structured telephone interview and correlated with scores using 2-way ANOVA. RESULTS: = .38). Outcome was assessed in 42 children with isolated corpus callosum agenesis and 9 with associated corpus callosum agenesis (mean age, 3.1 years). MR imaging scores correctly predicted developmental outcome in 90.7% of patients with isolated corpus callosum agenesis, improving neurodevelopmental risk stratification in corpus callosum agenesis. CONCLUSIONS: The scoring system is very reproducible and can differentiate isolated corpus callosum agenesis and associated isolated corpus callosum agenesis (significantly higher scores) but not between partial and complete corpus callosum agenesis. Scores correlated with outcome in isolated corpus callosum agenesis, but there were too few associated postnatal cases of isolated corpus callosum agenesis to draw conclusions in this group.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".