Bibliographic record
Abstract
Prefrontal synthesis of the lateral prefrontal cortex is necessary to create new mental images. When this faculty is deficient, several disorders can occur. Autism spectrum disorder (ASD) is one such disorder, with severe cases leading to prefrontal paralysis. As the results of prefrontal paralysis are devastating and irreversible, there are multiple diagnostic tools available for ASD. In addition, the Vyshedskiy lab has developed a therapy app, Mental Imagery Therapy for Autism, that trains prefrontal synthesis in children with ASD. Data from the app has also uncovered several recommendations that can significantly impact language development in ASD children such as limiting TV watching, and encouraging pretend play. On the other side of the age spectrum, Alzheimer’s disease (AD) is a neurodegenerative disease affecting the elderly. While AD causes overall cognitive decline, it also affects prefrontal synthesis. As the disease is growing in prevalence as the population ages, diagnostic accessibility has increased in importance. To address a dearth of diagnostics, the Vyshedskiy lab has developed the Boston Cognitive Assessment (BoCA), a cognitive test that boasts several improvements over the current gold standard the Montreal Cognitive Assessment. Though pharmaceutical interventions are still a research target for AD, the most promising therapy of the moment is 40Hz light therapy. By offering this therapy in a convenient application format, the Vyshedskiy lab hopes to make it more accessible to people with AD, as well as anyone looking to preserve their cognitive health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.150 | 0.037 |
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".