Differential Diagnosis of Intracranial Cystic Lesions at Head US: Correlation with CT and MR Imaging
Bibliographic record
Abstract
The differential diagnosis of intracranial cystic lesions at head ultrasonography (US) includes a broad spectrum of conditions: (a) normal variants, (b) developmental cystic lesions, (c) cysts due to perinatal injury, (d) vascular cystlike structures, (e) hemorrhagic cysts, and (f) infectious cysts. These lesions vary in prevalence from common (cavum of the septum pellucidum, subependymal cyst, choroid plexus cyst) to rare (vein of Galen malformation). US can provide important information about the anatomic location, size, and shape of the lesions as well as their mass effect on adjacent structures. Differential diagnosis may be difficult because there is substantial overlap of US features between many of these conditions. However, if careful attention is paid to the location and characteristics of the cyst, a more specific diagnosis may be suggested. Understanding the spectrum of appearances of the various intracranial cystic lesions at head US improves the diagnostic yield, enables one to understand their pathogenesis, and facilitates patient care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".