Post-Traumatic Meningitis: Case-Based Review of Literature from Internists’ Perspective
Bibliographic record
Abstract
Most cases of post-traumatic meningitis (PTM) occur following immediate head trauma or neurosurgical procedures. Hence, internists do not often come across these patients. However, closed-head trauma can be associated with community-acquired meningitis (CAM), and this history can often be missed especially if it is remote or trivial in nature. Therefore, meticulous clinical assessment is necessary to identify cases of community-acquired PTM. Knowledge about pathophysiological, anatomical, and microbiological context of community-acquired PTM is required in order to manage these patients. The role of internist is to provide holistic management in these patients which includes not only antimicrobial treatment but also timely referral to surgical specialties if required as well as vaccination to prevent further episodes. Here, we present a case of CAM with remote history of close head trauma and cerebrospinal fluid rhinorrhea for years who was found to have base of skull (BOS) defect on imaging of skull. He was treated with antibiotics and referred to surgical specialties for repair of BOS defect as well as given pneumococcal vaccine to prevent further episodes of meningitis.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| 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) | 0.004 | 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".