Bibliographic record
Abstract
PURPOSE OF REVIEW: This article reviews the influence of age on the development and course of central nervous system (CNS) inflammatory diseases and relates these to genetic and epidemiologic factors and to the age-related properties of both the immune and nervous systems and their interactions. RECENT FINDINGS: There is increased recognition of the onset of multiple sclerosis, the prototype CNS inflammatory/demyelinating disease, outside the expected peak age in young adults. Study of childhood-onset MS cases with comparison to those with uniphasic acquired inflammatory demyelinating syndromes, has helped to better define the clinical spectrum of both types of disorders and provides opportunities to define mechanisms linking recognized genetic and environmental risk factors and disease. Studies of late-onset cases implicate the enhanced role of innate immune mechanisms and immune responses compartmentalized within the CNS in driving a more progressive disease course. The influence of age on incidence and course of multiple sclerosis is compared with the wider spectrum of CNS inflammatory disorders with identified pathogenic mechanisms. SUMMARY: Intrinsic properties of the immune and nervous systems, coupled with the impacts on these systems by environmental factors and by the consequences of CNS tissue injury, underlie the age-related differences in incidence and course of inflammatory/demyelinating disorders of the CNS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".