Probable Causative Agents and Demographic Patterns of Encephalitis, Meningitis, and Meningoencephalitis in a Single Tertiary Care Center
Bibliographic record
Abstract
Introduction Encephalitis, meningitis, and meningoencephalitis present significant challenges in clinical management owing to their diverse etiologies and potential complications. A high suspicion index is critical for guiding treatment strategies and improving patient outcomes. Understanding the demographic characteristics and frequency of causes of these conditions is essential to deliver optimized care. Objective This study aimed to investigate epidemiological causes and relative outcomes, including mortality, based on cultures, laboratory investigations, and demographic factors among patients with encephalitis, meningitis, and meningoencephalitis in a Saudi Arabian tertiary care center. Methods A retrospective cross-sectional study was conducted at King Abdulaziz Medical City (KAMC) in Jeddah, Saudi Arabia. Data were collected from patients admitted between April 2016 and December 2022 who met the specified inclusion criteria. Results Among 233 patients, meningitis was the most prevalent diagnosis (65.77%), with bacterial agents being the predominant causative agents (79.74%). Higher mortality was significant with pediatrics <5 years and adults >60 years. Conclusion This study provides valuable insights into the epidemiology and clinical outcomes of central neurological infections based on a Saudi Arabian cohort. These findings underscore the importance of an accurate diagnosis and tailored management strategies. Further studies are warranted to enhance our understanding and to inform more predictable characteristics targeted in optimizing healthcare delivery for patients with such conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".