Feasibility and Added Value of Fetal DTI Tractography in the Evaluation of an Isolated Short Corpus Callosum: Preliminary Results
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Prognosis of isolated short corpus callosum is challenging. Our aim was to assess whether fetal DTI tractography can distinguish callosal dysplasia from variants of normal callosal development in fetuses with an isolated short corpus callosum. MATERIALS AND METHODS: This was a retrospective study of 37 cases referred for fetal DTI at 30.4 weeks (range, 25–34 weeks) because of an isolated short corpus callosum less than the 5th percentile by sonography at 26 weeks (range, 22–31 weeks). Tractography quality, the presence of Probst bundles, dysmorphic frontal horns, callosal length (internal cranial occipitofrontal dimension/length of the corpus callosum ratio), and callosal thickness were assessed. Cytogenetic data and neurodevelopmental follow-up were systematically reviewed. RESULTS: Thirty-three of 37 fetal DTIs distinguished the 2 groups: those with Probst bundles (Probst bundles+) in 13/33 cases (40%) and without Probst bundles (Probst bundles–) in 20/33 cases (60%). Internal cranial occipitofrontal dimension/length of the corpus callosum was significantly higher in Probst bundles+ than in Probst bundles–, with a threshold value determined at 3.75 for a sensitivity of 92% (95% CI, 77%–100%) and specificity of 85% (95% CI, 63%–100%). Callosal lipomas (4/4) were all in the Probst bundles– group. More genetic anomalies were found in the Probst bundles+ than in Probst bundles– group (23% versus 10%, P = .08). CONCLUSIONS: Fetal DTI, combined with anatomic, cytogenetic, and clinical characteristics could suggest the possibility of classifying an isolated short corpus callosum as callosal dysplasia and a variant of normal callosal development.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".