Immune checkpoint inhibitor-induced encephalitis with dostarlimab in two patients: Case series
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) are being used increasingly in the treatment of several cancers and have been associated with neurological complications including immune checkpoint inhibitor-induced encephalitis (ICI-iE). We present two cases of ICI-iE with the novel agent dostarlimab, which to our knowledge are the first reported with this agent. These cases add to the growing body of literature on ICI-iE, demonstrating two cases of meningoencephalitis associated with the novel agent dostarlimab treated successfully with prednisone. As imaging studies may be unrevealing, clinicians must maintain a high index of suspicion for ICI-iE in any patient who develops altered mental status on ICI therapy, with low threshold to obtain lumbar puncture for evidence of inflammatory CSF and to exclude other causes. It is important to note that many neurological presentations of ICIs can also be secondary to tumor metastasis and other paraneoplastic syndromes, making the diagnosis challenging. Prognosis can be good with early recognition and treatment with corticosteroids. Whether patients can be rechallenged with ICI is to be determined in larger studies given the rarity of this complication.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| 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".