Comparison of Infectious and Noninfectious Intracranial Caseating Granulomas
Bibliographic record
Abstract
Although caseating granulomas are classically associated with infectious processes, a subgroup of intracranial caseating granulomas without identifiable infectious pathology (ICGN) is described. We aimed to identify clinical, laboratory, radiological and histological markers with potential to distinguish patients with ICGN from those with intracranial caseating granulomas with infectious etiology (ICGI) on tissue microbiological examinations. In a referral hospital setting, we identified 11 patients with ICGNs and 6 patients with ICGI over an 11-year period. The two groups had similar demographics (other than higher infection risk factors in ICGIs), clinical presentation, serology, location of lesions and cellular composition of the inflammatory infiltrate. Significant differences were the homogenous vs. ring pattern of enhancement on neuroimaging and small (<1 mm) vs. large (>1 mm) area of necrosis on histological examination, in ICGNs and ICGIs, respectively. The dichotomy was best reflected in the response of ICGNs to immunomodulatory and not antimicrobial treatment and the reverse pattern in ICGIs. Based on these findings, we suggest a scheme for the diagnosis of ICGN: (i) caseating granulomas with areas of necrosis predominantly <1 mm in diameter; (ii) absence of an identifiable infectious agent in extensive tissue examinations; and (iii) no clinical and radiological response within 2 months of appropriate antimicrobial treatment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".