Corroboration of Normal and Abnormal Fetal Cerebral Lamination on Postmortem MR Imaging with Postmortem Examination
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The presence of normal fetal cerebral lamination of the germinal matrix, intermediate zone, subplate layer, and cortex can be used as a marker of normal fetal cerebral development. Our aim was to compare postmortem MR imaging assessment of normal and abnormal fetal cerebral lamination on T1- and T2-weighted images with histopathology. MATERIALS AND METHODS: Fifty-five formalin-fixed brains from postmortem fetuses, ranging from 16 to 30 weeks' gestational age, mean of 23 weeks, underwent T1- and T2- weighted MR imaging and subsequent sectioning and histologic examination. The cerebral lamination was graded as normal or abnormal on T1- and T2-weighted imaging and compared with postmortem findings. The sensitivity, specificity, and positive and negative predictive values of T1 and T2 assessment of cerebral lamination were calculated. RESULTS: Twenty-six fetuses had abnormal and 29 had normal cerebral lamination on histology. On T1, the overall sensitivity, specificity, and positive and negative predictive values of evaluating cerebral lamination were 96.15%(CI, 78.42%-99.80%), 89.66%(CI, 71.50%-97.29%), 89.29%(CI, 70.63%-97.19%), and 96.29%(CI, 79.11%-99.80%), respectively. On T2, the overall sensitivity, specificity, and positive and negative predictive values of evaluating cerebral lamination were 73.08%(CI, 51.95%-87.65%), 96.55%(CI, 80.37%-99.82%), 95.00%(CI, 73.06%-99.74%), and 80.00%(CI, 62.54%-90.94%), respectively. CONCLUSIONS: Postmortem MR imaging has high sensitivity, specificity, and positive and negative predictive values in assessing fetal cerebral lamination compared with histology. T1-weighted imaging has a higher sensitivity and negative predictive value, while T2-weighted imaging has a higher specificity and positive predictive value.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".