A validation study of disease definitions for immune checkpoint inhibitor neurotoxicity
Bibliographic record
Abstract
<h3></h3> Immune checkpoint inhibitors (ICIs) have transformed cancer outcomes. By targeting immune-cell- surface receptors, they augment the immune response to tumour cells but can cause range of immune-related (IR) toxicities. Neurological toxicity occurs in 1–12% and is much less frequent than other tissue IR-toxicity but carries a higher risk of long-term morbidity and mortality. Prompt and <b>c orrect</b> identification of IR-neurotoxicity is essential to accessing treatment, optimizing outcome, avoiding adverse effects of immunosuppression if not indicated and facilitating ongoing ICI therapy where appropriate. [1] Guidon et al suggest criteria for diagnosis of IR-neurotoxicity [2]: onset within 6–12 months of exposure, AND Improvement with corticosteroids SUPPORTED BY presence of neural antibodies, paraneoplastic neurological syndrome. The NHNN ICI neurotoxicity service has received 100 referrals between 2019–2023. Thirty-two individuals (32%) were diagnosed with IR-neurotoxicity on exhaustive assessment and close clinical follow up. Application of the published diagnostic definitions to this cohort reveals Se:84.4%,Sp:73.5%.,PPV:60%,NPV:90.9% With very low additive benefit from the supportive components. Given the relative rarity of IR-neurotoxicity with ICI (1% in monotherapy) we suggest careful clinical assessment superior to use of broad diagnostic definitions. The correct exclusion of IR-neurotoxicity (specificity) is VERY important, to avoid adverse effects of high-dose corticosteroids and discontinuation of effective anti-cancer immunotherapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".