Growth dynamics of plexiform neurofibromas: a retrospective cohort study of 201 patients with neurofibromatosis 1
Bibliographic record
Abstract
BACKGROUND: To examine the natural growth dynamics of internal plexiform neurofibromas (PNs) in patients with neurofibromatosis 1 (NF1). METHODS: Two hundred and one NF1 patients underwent whole body MRI (WBMRI). Tumour burden was estimated volumetrically. Non-parametric Spearman's rho correlation coefficients were used to analyse the relationship of growth rate to tumour volume and age. Chi-squared and Mann-Whitney U tests were used for analysing the association of tumour occurrence with sex or age. Chi-squared tests were used to analyse the association of tumour growth with age group. RESULTS: Seventy-one of 171 patients with serial WBMRI exams had internal PNs (median follow up 2.2 years [1.1 to 4.9 years]). Median whole body tumour volume was 86.4 mL [5.2 to 5878.5 mL]) with a median growth rate of 3.7%/year (-13.4 to 111%/year) that correlated with larger whole body tumour volume (P<0.001) and lower age (P=0.004). No new PNs developed in 273.0 patient-years among patients without tumours. Rate of new tumour development among patients with PNs was 0.6%/year (95% confidence interval 0.02 to 3.4%). Twenty-seven (13.5%) tumours increased significantly and were more frequent among children (P<0.001). Growth rate of tumours was inversely correlated with age (Spearman's rho=-0.330, P<0.001). Seventy-one (35.5%) tumours had smaller volumes on follow up (median -3.4%/year [-0.07% to -35.9%/year]). CONCLUSION: Children with NF1 and internal PNs are at risk for tumour growth. Most PNs grow slowly or not at all, and some decrease in size. New tumours are infrequent in NF1 patients with PNs and unlikely in patients without PNs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".