'Brain Fogging' in Special Needs Children- a Post-Covid Neurobiological Enigma
Bibliographic record
Abstract
COVID-19 is associated with clinically significant symptoms- post-Covid syndromes, despite its immediate resolution. COVID-19 cases continue to experience the after-effects of the disease including multi-system dysfunctions, thus causing a drain-out of health resources in dealing with its aftermath. Post-COVID-19 syndrome is determined as signs and symptoms that appear during or after an infection consistent with SARS-CoV-2 disease, persist for more than 12 weeks, and are not explained by an alternative diagnosis. This review presents the most frequent neurological complaints associated with COVID-19 along with a recondite of brain fog. In the context of post-COVID-19, Pediatricians, as well as parents, should be aware of a wide spectrum of neurological COVID-19 signs and its association with impairments, commonly called ‘ brain fog’. Further, investigation of the molecular mechanism behind brain fog is suggested. Targeting the newly identified mechanisms may aid in finding newer molecules for treating brain fog. Though in adult Montreal cognitive tests for executive dysfunctions and
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".