Challenges in Diagnosing and Treating Granulomatous Amebic Encephalitis: A Case Report of Fatal <i>Acanthamoeba</i> spp. Encephalitis in an Immunocompetent Patient
Bibliographic record
Abstract
Granulomatous amebic encephalitis (GAE) is a rare but fatal infection of the central nervous system with a high mortality rate, due to free-living amoebae that are pathogenic to humans and ubiquitous in the environment. In this paper, we report the case of an immunocompetent adult female with no relevant medical history, who presented with acute symptoms resembling a stroke, including altered mental status, slurred speech, brutal proportional right hemiplegia, facial droop and focal to bilateral tonic-clonic seizures, all concomitant with high fever. Subsequent magnetic resonance imaging of the brain revealed diffuse multiple nodular lesions both above and below the tentorium. The initial cerebrospinal fluid (CSF) profile was of no great significance. However, microscopic examination of CSF was able to identify the presence of amoeboid microorganisms and cyst formation, suggestive of a telluric amoeba’s infection. The patient was then treated with a combination of fluconazole and trimethoprim-sulfamethoxazole, but her neurological state continued to decline until she passed away from GAE. In our case report, we highlight the difficulties clinicians encountered in managing this disease, as the clinical and radiological presentation are nonspecific, in addition to the lack of clear therapeutic guidelines after diagnosis. Our findings point up the urgent need for more precise diagnostic criteria and comprehensive treatment protocols to improve patient outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".