Fetal MRI assessment of posterior fossa anomalies: A review
Bibliographic record
Abstract
Prenatal ultrasound (US) is the first prenatal imaging tool for screening and evaluation of posterior fossa malformations since it is noninvasive, widely available, and safe for both mother and child. Fetal MRI is a widely used secondary technique to confirm, correct, or complement questionable US findings and plays an essential role in evaluating fetuses with suspected US findings and /or positive family history. The main sequences of fetal MRI consist of T2-weighted (T2w) ultrafast, single-shot sequences. Axial, coronal, and sagittal images are typically acquired allowing for a detailed evaluation of the posterior fossa contents. Also, various complimentary sequences, such as T1w, T2*w gradient sequences, or advanced techniques, including diffusion-weighted imaging, diffusion tensor imaging, and magnetic resonance spectroscopy, may provide additional information based on the studied malformation. Inclusion of these techniques should be done with careful risk-benefit analysis. The use of fetal MRI also aims to evaluate for associated anomalies. In addition, prenatal diagnosis of posterior fossa malformations is still a challenge but advances in knowledge in human developmental anatomy, genetic, and imaging recognition patterns have enabled us to shed some light on prognostic information that will help with the counseling of families. Finally, high-resolution late third trimester fetal MRI offers a safe alternative to early postnatal MR imaging, basically taking advantage of the uterine environment as a kind of "maternal incubator." Our goal is to discuss the spectrum of prenatal posterior fossa pathologies that can be studied by fetal MRI and their key neuroimaging features.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".