Characteristics, outcome and treatments with cranial pachymeningitis
Bibliographic record
Abstract
The aim of this study was to determine the characteristics, treatment, and outcome according to each etiology of pachymeningitis.We conducted a retrospective multicenter French nationwide study between 2000 and 2016 to describe the characteristics, outcome, and treatment of pachymeningitis.We included 60 patients (median age 55.5 years; interquartile range [IQR] 30-80, female/male ratio 0.43). Neurologic signs were present in 59 patients (98%) and consisted of headache in 43 (72%), cranial nerve palsy in 33 (55%), confusion in 10 (17%), seizures in 7 (12%), and focal neurologic signs in 9 (15%). Fever and weight loss were present in 8 (13%) and 13 cases (22%), respectively. Cerebral venous thrombosis was present in 8 cases (13%). Analysis of cerebrospinal fluid showed moderate hyperproteinorachia (median 0.68 g/L; IQR 0.46-3.2) with or without pleiocytosis. Diagnosis included idiopathic pachymeningitis (n = 18; 30%); granulomatosis with polyangiitis (n = 13; 17%); Erdheim-Chester disease (n = 10; 17%); IgG4-related disease and tuberculosis (n = 3; 5% each); Rosai-Dofman disease, microscopic polyangiitis, and sarcoidosis (n = 2, 3% each); cryptococcal meningitis, Lyme disease, ear-nose-throat infection, postlumbar puncture, low spinal-fluid pressure syndrome, and lymphoma (n = 1 each). We found no difference in demographics and neurologic presentation among idiopathic pachymeningitis, Erdheim-Chester disease, and granulomatosis with polyangiitis. In contrast, frequencies were lower with idiopathic pachymeningitis than Erdheim-Chester disease for general signs (6% and 40%, respectively, P = .041) and complete neurologic response (0% vs 39%, P = .045).The detection of extraneurologic signs and routine screening are needed to classify the pachymeningitis origin. Prospective studies are warranted to determine the best treatment in each case.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".