Disseminated Spinal Epidural Abscess in an Immunocompetent Individual: A Case Report and Review of the Literature
Bibliographic record
Abstract
Spinal epidural abscess (SEA) is an uncommon pyogenic infection, localized between the dura mater and vertebral periosteum, leading to significant morbidity and mortality. SEA development is connected with medical comorbidities and risk factors facilitating bacterial dissemination; multiple factors are believed to play a role, including aging, increased alcohol abuse, use of intravenous drugs, a greater prevalence of medical comorbidities, and increased rates of spinal surgery that furthers iatrogenic spinal infection. Here, we have reported the first known case of disseminated SEA in an immunocompetent individual. A 33-year-old Japanese woman visited our hospital due to 1 week of continuous fever, low back pain, and numbness of the entire left lower limb. She was diagnosed with disseminated SEA by complete spine magnetic resonance imaging scan, of unknown origin. She was treated for 13 days with piperacillin-tazobactam, then for 16 days with levofloxacin tablets; ultimately, she recovered without treatment complications. This case highlights the complicated pathology, diagnosis, and treatment of SEA. In addition, this case suggests the need for a careful and detailed examination when encountering patients presenting with fever, low back pain even in an immunocompetent individual; we should thoroughly investigate, including further image investigations, bacteriological and pathologic examination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".