Elevated CK from immune checkpoint inhibitor- related hypophysitis: a case report
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs), have emerged to the forefront of management for various advanced cancers, such as melanoma, lung cancer and renal cell carcinoma. Immune checkpoints such as CTLA-4 and PD-1 serve to inhibit T cell activation and signaling; therefore through blockade of these pathways, ICIs promote anti-tumour immune activation. However, as a result of T cell disinhibition, ICIs have been reported to cause immune related adverse events (irAEs) affecting numerous organ systems. One of the most serious and potentially life-threatening irAE is inflammatory myositis. Myositis, which generally presents with progressive proximal muscle weakness and elevated serum creatine kinase (CK), has been reported in <1% of patients who have received ICI therapy. A rare cause of elevated CK is adrenal insufficiency, which has been reported in up to 6% of ICI users. Here we report a case of ICI-related hypophysitis related myopathy that was initially misdiagnosed as ICI-associated inflammatory myositis. This case illustrates the importance of considering a wide differential when assessing hyperCKemia in the setting of ICI use.
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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 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| 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".