Cerebral abscesses imaging: A practical approach
Bibliographic record
Abstract
Brain abscesses (BAs) are focal infections of the central nervous system (CNS) that start as a localised area of weakening of the brain parenchyma (cerebritis) and develops into a collection of pus surrounded by a capsule. Pyogenic (bacterial) BAs represent the majority of all BAs; in some cases, the diagnostic and therapeutic management can be challenging. Imaging has a primary role in differentiating BAs from other lesions. Conventional magnetic resonance imaging (cMRI) is essential for the identification of the lesion, its localisation and its morphological features. However, cMRI does not allow to reliably differentiate BAs from other intracranial mass lesions such as necrotic tumours. Advanced sequences, such as diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI) and proton MR spectroscopy (1H-MRS) are very useful in the differential diagnosis from other brain lesions, such as non-pyogenic abscesses or necrotic tumours, and provide essential information on structural, vascular and meta-bolic characteristics allowing greater neuroradiological confidence. The aim of this pictorial review is to provide a practical approach showing the added value of more advanced MRI techniques in their diagnostic management.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".