Neuroimages and Neuropathology of a Stroke-Like Cerebral Lymphomatoid Granulomatosis
Bibliographic record
Abstract
A 70-year-old man presented to the Emergency Department reporting the acute onset of non-fluent aphasia, hyposthenia, and hemi-anesthesia of the right body. Brain computerized tomography revealed a subcortical hypodense lesion in the middle cerebral artery territory. Neck ultrasounds of internal and external carotid arteries and of the vertebral arteries showed a focal moderate stenosis of the left internal carotid artery due to a soft atheromasic plaque. These findings that were initially consistent with a diagnosis of an ischemic stroke were not confirmed by magnetic resonance (MR). The latter showed an hyperintense lesion on FLAIR and T2-weighted sequences located in the left centrum semiovale, corona radiata, and thalamus, with a well-defined regular rim and a mild compressive effect on the lateral ventricle, with diffusivity restriction but without ADC reduction and with a punctate and serpiginous gadolinium enhancement on T1 sequences (Figure 1). Within the first day of observation, the patient started complaining progressive mental deterioration, in absence of any other possible causes, and a total body CT scan excluded any other organ involvement. Patient was then referred to the neurosurgeon in order to perform a brain biopsy. The neuropathology was compatible with the diagnosis of cerebral lymphomatoid granulomatosis (LG) (Figure 1).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".