How Accurately Does Current Fetal Imaging Identify Posterior Fossa Anomalies?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The first objective of our study was to describe the prevalence and spectrum of posterior fossa anomalies over 5 years in a major fetal care center where the referral diagnosis (by fetal sonography) was investigated by fetal MRI and, if confirmed, by postnatal MRI if possible. The second objective was to assess the accuracy with which fetal MRI predicts postnatal MRI findings in this population. MATERIALS AND METHODS: We retrospectively identified all cases of suspected fetal posterior fossa anomalies referred to our center from 2002 through 2006. We reviewed maternal, fetal, neonatal, and follow-up records of all cases and fetal and early postnatal imaging studies. RESULTS: Of the 90 cases of suspected fetal posterior fossa anomalies (by fetal sonography) referred over the study period, 60 (67%) were confirmed by fetal MRI. Of 42 live-born infants, 39 (93%) underwent postnatal MRI. There was complete agreement in fetal and postnatal MRI diagnoses in 23 infants (59%). In 16 cases (41%), fetal and postnatal MRI diagnoses disagreed; postnatal MRI excluded fetal MRI diagnoses in six cases (15%) and revealed additional anomalies in 10 cases (26%). CONCLUSION: Although a valuable adjunct to fetal sonography in cases of suspected posterior fossa anomaly, current fetal MRI, particularly in early gestation, has limitations in accurately predicting postnatal MRI abnormalities. Advancing the accuracy of MRI for the diagnosis of posterior fossa anomalies will require greater understanding of normal brain development and improved tissue resolution of fetal MRI. During the interim, our findings strongly support the need for postnatal MRI follow-up in cases with suspected posterior fossa anomalies by fetal MRI.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it