Immune checkpoint inhibitor neurotoxicity clinic: a service evaluation
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) are a form of cancer immunotherapy which have revolutionised the treatment and outcome of multiple solid organ malignancies [1], their use is continuing to expand. We have established a tertiary service specifically for seeing patients with suspected neurological toxicity secondary to ICIs. Here we report our findings. Between 2019 and 2023, 100 patients were seen within our service. The median time from referral by the oncologist to being seen in clinic was 10.5 days [IQR: 5 to 35 days], compared to median outpatient waiting times in London trust of 84- 154 days [2] and 32 weeks for General Neurology in NHNN/UCLH. Of the 32/100 with ICI-neurotoxicity the median time from referral to diagnosis was 25 days [6 to 64 days]. All patients were treated with steroids, and the majority were commenced on steroids prior to assessment by a neurologist. Additional treatments by neurologists included IVIG (6 patients), plasma exchange (4 patients) and steroid-sparing agents (4 patients). The median mRS at 6 months 1 [1 to 3]. This novel, responsive and patient-centred model for neurological immune- related toxicity has a positive impact on patient outcome which is reported as 30- 50% mortality in the literature.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".