Bibliographic record
Abstract
Introduction History Neurofibromatosis (NF) is a common neurocutaneous disorder that has an incidence of approximately 1 in 4000 (Mulvihill et al ., 1990). Although NF has been postulated to have as many as eight different forms (Riccardi & Eichner, 1986), this classification system has not been widely adopted. Neurofibromatosis is a group of genetic disorders including NF type I (NF-I), NF type II (NF-II), and multiple schwannomatosis, each with distinctly different genetic mutations and pathologic bases. The NF-I gene is nearly ubiquitous in human tissues and so impacts virtually all organ systems. NF-I is particularly interesting to neurocognitive scientists because of its characteristic phenotypical abnormalities in development of form and function in brain. NF-II and multiple schwannomatosis are essentially disorders of cranial nerves, peripheral nerves, and meningeal tissues with no associated cognitive abnormalities and so these disorders will be excluded from this discussion. The original term neurofibromatosis was derived at the turn of the last century but the disorder is also called von Recklinghausen's disease because the condition was described in the late 1800s clinically and scientifically by Friedrich Daniel von Recklinghausen (Cawthon et al ., 1990; Crump, 1981; Viskochil et al ., 1990). The molecular genetic basis of distinguishing clinical features of NF-I was localized to chromosome 17 in 1990 by two teams of investigators (Viskochil et al ., 1990; Wallace et al ., 1990).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.080 | 0.046 |
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".