Internal tumor burden in neurofibromatosis Type I patients with large <i>NF1</i> deletions
Bibliographic record
Abstract
Neurofibromatosis Type 1 (NF1) is a frequent tumor suppressor gene disorder characterized by multiple benign tumors and high risk of malignancy. Internal tumor burden is a major disease-associated manifestation and can be most adequately assessed by magnetic resonance imaging of the whole body. Approximately 5% of NF1 patients have constitutional large NF1-deletions that are generally associated with more severe clinical manifestations. Here, we investigated whether these deletion patients also have more and/or larger internal tumors by assessing internal tumors and their total volume (exclusive of cutaneous and subcutaneous) in 38 NF1 deletion patients (including eight mosaic cases) and 114 age- and gender-matched NF1 patients without deletions. The incidence of internal tumors was significantly lower in mosaic deletion patients (1/8 = 13%) but did not differ between the 30 nonmosaic deletion patients and the 90 age- and gender-matched NF1 patients without large deletions used as controls. Neither the number nor the total volume of tumors per patient differed significantly between the latter two groups. However, extremely high tumor burden (>3,000 ml) was significantly more frequent among nonmosaic NF1 deletion patients than among NF1 patients without large deletions (13% vs. 1%, P = 0.014). Thus, as a group, patients with NF1 deletions do not exhibit a significantly higher internal tumor burden than NF1 patients without such deletions. However, deletion patients can frequently have extremely large internal tumors and thus demand special attention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".