CLIPPERS and its mimics: evaluation of new criteria for the diagnosis of CLIPPERS
Bibliographic record
Abstract
OBJECTIVE: To evaluate the accuracy of the recently proposed diagnostic criteria for chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids (CLIPPERS). METHODS: We enrolled 42 patients with hindbrain punctate and/or linear enhancements (<3 mm in diameter) and tested the CLIPPERS criteria. RESULTS: After a median follow-up of 50 months (IQR 25-82), 13 out of 42 patients were CLIPPERS-mimics: systemic and central nervous system lymphomas (n=7), primary central nervous system angiitis (n=4) and autoimmune gliopathies (n=2). The sensitivity and specificity of the CLIPPERS criteria were 93% and 69%, respectively. Nodular enhancement ( ≥ 3 mm in diameter), considered as a red flag in CLIPPERS criteria, was present in 4 out of 13 CLIPPERS-mimics but also in 2 out of 29 patients with CLIPPERS, explaining the lack of sensitivity. Four out of 13 CLIPPERS-mimics who initially met the CLIPPERS criteria displayed red flags at the second attack with a median time of 5.5 months (min 3, max 18), explaining the lack of specificity. One of these four patients had antimyelin oligodendrocyte glycoprotein antibodies, and the three remaining patients relapsed despite a daily dose of prednisone/prednisolone ≥ 30 mg and a biopsy targeting atypical enhancing lesions revealed a lymphoma. CONCLUSIONS: Our study highlights that (1) nodular enhancement should be considered more as an unusual finding than a red flag excluding the diagnosis of CLIPPERS; (2) red flags may occur up to 18 months after disease onset; (3) as opposed to CLIPPERS-mimics, no relapse occurs when the daily dose of prednisone/prednisolone is ≥ 30 mg; and (4) brain biopsy should target an atypical enhancing lesion when non-invasive investigations remain inconclusive.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".