Editorial: Viral impact on CNS: mechanisms of immune dysfunction and cognitive decline
Bibliographic record
Abstract
Recently, the contribution of viruses to neuropathology and cognitive decline has garnered significant 26 interest with viral infection at least in part, with pathogenesis of dementia, multiple 27 sclerosis and virus-specific cognitive impairment (1)(2)(3)(4). Neuropathology can occur during acute, 28 chronic and latent infection and, in some cases, even in the presence of antiviral therapy. However, the 29 precise mechanisms by which specific viruses induce neuropathology and cognitive dysfunction 30 remain unclear. This underscores the need to elucidate the underlying processes in order to develop 31 effective therapeutic strategies. In this Research Topic, we have collated a series of manuscripts that 32 assess the contribution of various viruses including Human Immunodeficiency Virus (HIV), SARS-33CoV-2, and others, to neuroinflammation, neuropathology and cognitive disorders. 34As mentioned above, the role of viruses in neuroinflammation and neuropathology has gained 36 significant attention in recent years.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".