Analysis of neurofibromatosis 1 (NF1) lesions by body segment
Bibliographic record
Abstract
Café-au-lait spots and neurofibromas are defining features of neurofibromatosis 1 (NF1), but they vary greatly in number, size, and clinical importance from patient to patient. The cause of this variability is unknown. We tested the hypotheses that development of these lesions is influenced by local or familial factors. The presence or absence of café-au-lait spots, cutaneous neurofibromas, and diffuse plexiform neurofibromas was recorded for each of 10 divisions of the body surface in 547 NF1 patients, including 117 affected individuals in 52 families. We used stratified Mantel-Haenszel tests to look for local associations between the presence of diffuse plexiform neurofibromas, cutaneous neurofibromas, and café-au-lait spots in individual body segments of NF1 patients. We used a random effects model to obtain intrafamilial correlation coefficients for the age-adjusted number of body divisions affected with each of the three lesions. No significant association was observed between the occurrence of cutaneous and diffuse plexiform neurofibromas, between café-au-lait spots and cutaneous neurofibromas, or between café-au-lait spots and plexiform neurofibromas in the same body segment. The correlation among relatives in the number of body segments affected with café-au-lait spots was 0.45 (95% confidence interval [CI] = 0.18-0.71), with cutaneous neurofibromas, 0.37 (95% CI = 0.15-0.55), and with plexiform neurofibromas, 0.35 (95% CI = 0.15-0.57). We conclude that the development of café-au-lait spots, cutaneous neurofibromas, and plexiform neurofibromas are spatially independent in NF1 patients but that the development of all three lesions is influenced by familial factors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".