Spinal calcifying pseudoneoplasm of the neuraxis (CAPNON) associated with facet joint pathologies: CAPNON diagnostic and pathogenic insights
Bibliographic record
Abstract
Calcifying pseudoneoplasm of the neuraxis (CAPNON) is a rare tumor-like fibro-osseous lesion that can develop anywhere in the neuraxis. Approximately a half of reported CAPNONs developed in the spinal region, mostly close to the facet joint (FJ). The diagnosis of spinal CAPNONs is challenging given the existence of mimics and associated pathologies including calcific degeneration of the FJ ligaments (DFJL) and synovial cysts (SCs). The pathogenesis of CAPNON remains elusive, although there have been a few hypotheses including degenerative, reactive, proliferative and immune-mediated processes. Our present study examined clinical, radiological and pathological features of 12 spinal CAPNONs in comparison to 9 DFJL foci, and diagnostic and pathogenic relationship between CAPNONs and FJ pathologies. On imaging, CAPNONs were all tumor-like and typically bigger than DFJL foci. All CAPNONs showed pathologically diagnostic features including characteristic cores, consistently identifiable core-surrounding/peripheral palisading of macrophages and other cells including multinucleated giant cells, variable infiltration of CD8+ T-cells, and multifocal immunopositivity of neurofilament light chain (NF-L). These features were absent or limited in the DFJL foci with statistically significant differences from CAPNONs, except calcifications. Spinal CAPNONs co-existed with DFJL foci in all cases; some had transitional foci with overlapping focal CAPNON and DFJL-like features. These findings, along with our previously reported relationship between CAPNONs and SCs, suggest that spinal CAPNONs may occur in association with or in transition from calcifying/calcified degenerative lesions of FJ ligaments and/or SCs when a reactive proliferative process is complemented by other pathogenic changes such as immune-mediated pathology and NF-L deposition/expression.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".