Brain Abscess in a Patient With Radiotherapy-Treated Adenoid Cystic Carcinoma: A Misdiagnosis Case Report and Review of the Literature
Bibliographic record
Abstract
Brain abscesses are a relatively rare entity with an estimated incidence of 0.3 to 1.3 per 100,000 people per year. Brain abscesses arise from direct contiguous spread, hematogenous spread, neurosurgical procedures, open traumatic brain injuries, and cryptogenic sources. Early identification is pivotal, as delayed diagnosis and treatment lead to a very poor prognosis. Our case illustrates an elderly gentleman with a history of adenoid cystic carcinoma (ACC) of the oropharyngeal palate who presented to an outside hospital with severe headaches and was found to have a questionable metastatic lesion to his left temporal region. He was discharged with a course of steroids. Weeks later his headaches persisted, mentation further declined and repeat imaging revealed the same abnormal lesion. He subsequently underwent a craniotomy and was found to have a significant temporal abscess and empyema, which were evacuated. Post-operatively his course was complicated by status epilepticus requiring intubation and he was ultimately placed on hospice care. Our case illustrates the importance of early recognition and intervention for suspicious lesions, particularly when predisposing risk factors exist. J Neurol Res. 2020;10(5):199-202 doi: https://doi.org/10.14740/jnr620
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".