Foetal “black bone” MRI: utility in assessment of the foetal spine
Bibliographic record
Abstract
OBJECTIVE: Foetal CT has recently been added to the foetal imaging armamentarium, but this carries with it the risks of ionizing radiation, both to the mother and the foetus. Foetal "black bone" MRI is a new technique that allows assessment of the foetal skeleton without the risk of exposure to ionizing radiation and is a potential new sequence in foetal MRI examination. METHODS: Retrospective review of all foetal MRI studies over the past 4- to 5-year period identified 36 cases where susceptibility weighted imaging was used. Cases were selected from this group to demonstrate the potential utility of this sequence. RESULTS: This sequence is most frequently useful not only in the assessment of spinal abnormalities, most commonly the bony abnormalities in myelomeningocele, but also in cases of scoliosis, segmentation anomalies and sacrococcygeal teratoma. CONCLUSION: Although the utility of this sequence is still being evaluated, it provides excellent contrast between the mineralized skeleton and surrounding soft tissues compared with standard half Fourier acquisition single-shot turbo-spin echo sequences. Further assessment is required to determine whether black bone MRI can more accurately evaluate the level of bony defect in spina bifida aperta, an important prognostic factor. Potential further uses include the assessment of skeletal dysplasias, evaluation of the skull base and craniofacial skeleton in certain congenital anomalies and the post-mortem evaluation of the foetal skeleton potentially obviating the need for necropsy. ADVANCES IN KNOWLEDGE: Foetal black bone MRI can be performed using susceptibility weighted imaging and allows better demonstration of the mineralized skeleton compared with standard sequences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".