Skull Base Calcifying Pseudoneoplasms of the Neuraxis: Two Case Reports and a Systematic Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Calcifying pseudoneoplasm of the neuraxis (CAPNON) is a rare tumefactive lesion. CAPNONs can mimic calcified meningiomas at the skull base. METHODS: Here, we report two cases of CAPNON and present a systematic review of the literature on skull base CAPNONs, to compare CAPNONs with calcified meningiomas. RESULTS: Case 1: A 57-year-old man presented with right-sided lower cranial neuropathies and gait ataxia. He underwent a subtotal resection of a right cerebellopontine angle lesion, with significant improvement of his gait ataxia. However, his cranial neuropathies persisted. Pathological examination of the lesion was diagnostic of CAPNON, with the entrapped nerve fibers identified at the periphery of the lesion, correlating with the patient's cranial neuropathy. Case 2: A 70-year-old man presented with progressive headache, gait difficulty, and cognitive impairment. He underwent a frontotemporal craniotomy for a near-total resection of his right basal frontal CAPNON. He remained neurologically stable 7 years after the initial resection without evidence of disease recurrence. We analyzed 24 reported CAPNONs at the skull base in our systematic review of the literature. Cranial neuropathies were present in 11 (45.8%) patients. Outcomes regarding cranial neuropathies were documented in six patients: two had sacrifice of the nerve function with surgical approaches and four had persistent cranial neuropathies. CONCLUSION: While CAPNON can radiologically and grossly mimic calcified meningiomas, they are two distinctly different pathologies. CAPNONs located at the skull base are commonly associated with cranial neuropathies, which may be difficult to reverse despite surgical intervention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 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".