Implications of Inflammatory Processes on a Developing Central Nervous System in <scp>Childhood‐Onset</scp> Systemic Lupus Erythematosus
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that is increasingly affecting pediatric and adult populations. Neuropsychiatric manifestations (ie, cognitive dysfunction and mood disorders) appear to occur with greater severity and poorer prognosis in childhood-onset SLE (cSLE) versus adult-onset SLE, negatively impacting school function, self-management, and psychosocial health, as well as lifelong health-related quality of life. In this review, we describe pathogenic mechanisms active in cSLE, such as maladaptive inflammatory processes and ischemia, which are hypothesized to underpin central phenotypes in patients with cSLE, and the role of alterations in protective central nervous system (CNS) barriers (ie, the blood-brain barrier) are also discussed. Recent findings derived from novel neuroimaging approaches are highlighted because the methods employed in these studies hold potential for identifying CNS abnormalities that would otherwise remain undetected with conventional multiple resonance imaging studies (eg, T2-weighted or fluid-attenuated inversion recovery sequences). Furthermore, we propose that a more robust presentation of neuropsychiatric symptoms in cSLE is in part due to the harmful impact of a chronic inflammatory insult on a developing CNS. Although the immature status of the CNS may leave patients with cSLE more vulnerable to harboring neuropsychiatric manifestations, the same property may represent a greater urgency to reverse the maladaptive effects associated with a proneuroinflammatory state, provided that effective diagnostic tools and treatment strategies are available. Finally, considering the crosstalk among the CNS and other organ systems affected in cSLE, we postulate that a finer understanding of this interconnectivity and its role in the clinical presentation in cSLE is warranted.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".