Acute Limbic Encephalitis: Diagnostic and Management Implications
Bibliographic record
Abstract
OBJECTIVE: A link between cancer and limbic encephalitis (LE) has been proposed. The aim of this case presentation is to further explore this association by literature review, inform clinicians of the investigations necessary to arrive at a diagnosis and to emphasize the clinical importance of liaison between psychiatry and other disciplines involved in management. METHOD: The case report of a 55 year old Niuean male school teacher with an acute onset of confusion and personality change is presented. The clinical data was obtained from various sources including the emergency room, medical ward, psychiatric ward as well as from discussions with other physicians involved in the management of this case. Family members and friends were also contacted to obtain corroborative historical information. RESULTS: LE was diagnosed in this case based on clinical presentation with psychiatric symptoms, ruling out delirium due to infections, metabolic and other toxins as well as magnetic resonance imaging findings confirming temporal lobe changes. CONCLUSION: LE is a known paraneoplastic syndrome (PNS) that may precede the diagnosis of an underlying malignancy. Recent advances in laboratory technology now allow for antibodies to be identified in specific malignancies. This behooves the clinicians to maintain a high level of diagnostic suspicion so that timely interventions with oncology can follow.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".