Forensic Neuropathologic Phenotypes of Fungal Central Nervous System Infections
Bibliographic record
Abstract
ABSTRACT: Fungal infections of the central nervous system (FI-CNS) are life-threatening infections that most commonly affect immunocompromised individuals, but immunocompetent individuals may also be infected. Although FI-CNS are relatively rare, the prevalence of FI-CNS is on the rise because of the increasing number of transplant recipients, human immunodeficiency virus-infected individuals, and use of immunosuppressive therapies. Most cases of FI-CNS originate from outside the central nervous system. The etiologic fungi can be classified into 3 fungal groups: molds, dimorphic fungi, and yeasts. The clinical presentation of FI-CNS is highly variable and may be difficult to diagnose premortem. We present a case series of 3 patients, each infected by 1 representative species from each of the 3 fungal groups (Aspergillus species, Blastomyces species, Candida species) to illustrate different neuropathologic phenotypes of FI-CNS. All 3 patients had no history of immunodeficiency and were not suspected to have FI-CNS until they were diagnosed at autopsy. Fungal infections of the central nervous system are often fatal due to delayed diagnosis and diagnostic testing. Awareness of such poly-phenotypic manifestations of FI-CNS will be helpful in reducing delayed diagnosis. It is important for clinicians to include FI-CNS on the differential diagnosis when radiographic findings are nonspecific.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".