Quantitative associations of scalp and body subcutaneous neurofibromas with internal plexiform tumors in neurofibromatosis 1
Bibliographic record
Abstract
Internal plexiform neurofibromas are a major cause of adverse outcomes in patients with neurofibromatosis 1 (NF1). We investigated the relationship of the numbers of subcutaneous neurofibromas of the scalp or body to internal plexiform tumor volume in 120 NF1 patients who had undergone whole body magnetic resonance imaging (MRI). We identified internal plexiform neurofibromas in 55% of patients, subcutaneous neurofibromas of the body in 75%, and subcutaneous neurofibromas of the scalp in 45%. The number of subcutaneous neurofibromas of the body and scalp were associated with each other (Spearman's Rho = 0.36; P < 0.001). The presence of internal tumors was associated with the presence (odds ratio [OR] = 4.38, 95% confidence interval [CI] 2.04-9.86, P < 0.001) and number (OR = 1.06 per neurofibroma, 95% CI 1.02-1.13, P < 0.001) of subcutaneous neurofibromas of the scalp. The total internal tumor volume was associated with the number of subcutaneous neurofibromas of the body (OR = 1.00086 per neurofibroma, 1.000089-1.0016, P = 0.029) and of the scalp (OR = 1.056 per neurofibroma, 1.029-1.083, P < 0.0001). Numbers of subcutaneous neurofibromas of the scalp and body are associated with internal plexiform tumor burden in NF1. Recognition of these associations may improve clinical management by helping to identify patients who will benefit most from whole body MRI and more intense clinical surveillance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".