P.109 Meningioma with intraparenchymal abscess: a case report and review of the literaure
Bibliographic record
Abstract
Background: Intracranial intratumoural abscesses are rare occurrences typically treated with antibiotics and possible surgical resection. This study describes a meningioma-associated abscess and a review of the literature. Methods: Medical records and investigations were reviewed. A literature search of PubMed was completed. Results: A 56-year-old male presented with septic shock and dysuria. Urine culture isolated E. Coli, and he was treated with Ertapenem prior to discharge. A CT scan was ordered during hospitalization for unrelenting headaches, revealing a meningioma. Conservative management with follow-up as an outpatient was decided. However, he returned within two weeks with a fever and progressive left-sided weakness. A right frontal craniotomy for tumour resection was performed, and culture of necrotic-appearing tissue within the tumour revealed E. Coli. He was treated with Meropenem for six weeks, and at follow-up, the patient was asymptomatic. Our scoping review illustrated that 18 meningioma-associated abscesses have been reported in the literature since the first report in 1994. Conclusions: This case highlights the hematogenous spread of a urinary infection, resulting in an intratumoural abscess. Review of the literature indicated that, similarly, 39% of cases had recent or concurrant urinary tract infections. Future studies should seek to determine conclusive guidelines for diagnosing intratumoural abscesses.
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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 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".