Neurocognitive outcomes in neurofibromatosis clinical trials
Bibliographic record
Abstract
Neurofibromatosis type 1 (NF1) is associated with neurocognitive deficits that can impact everyday functioning of children, adolescents, and adults with this disease. However, there is little agreement regarding measures to use as cognitive endpoints in clinical trials. This article describes the work of the Neurocognitive Committee of the Response Evaluation in Neurofibromatosis and Schwannomatosis (REiNS) International Collaboration. The goal of this committee is to identify standardized and specific cognitive assessment tools for use in NF clinical trials. The committee first identified cognitive domains relevant to NF1 and prioritized attention as the first domain of focus given prior and current trends in NF1 cognitive clinical trials. Performance measures and behavioral rating questionnaires of attention were reviewed by the group using established criteria to assess patient characteristics, psychometric properties, and feasibility. The highest rated tests underwent side-by-side comparison. The Digit Span subtest from the Wechsler scales was given the highest ratings of the performance measures due to its good psychometrics, feasibility, utility across a wide age range, and extensive use in previous research. The Conners scales achieved the highest ratings of the behavioral questionnaires for similar reasons. Future articles will focus on other cognitive domains, with the ultimate goal of achieving agreement for cognitive endpoints that can be used across NF clinical trials.
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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 | 0.203 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".