A Chinese Collaborative Model for Accelerating Neurofibromatosis Type 1-Associated Research
Bibliographic record
Abstract
Abstract Neurofibromatosis type 1 (NF1) is a genetic disorder that affects multiple organ systems. Establishing a multidisciplinary center becomes essential for NF1 management. This study aims to introduce the progress and patient characteristics of the largest NF1 center in China. We retrieved NF1 patient data from 2013 to 2021, including basic personal information, date and department of first admission, location of tumors, and number of re-admission. A total of 725 patients were enrolled in this study, with a mean age of 23.8 years old. Patients were primarily admitted at the age of adolescence and young adulthood. There was not much difference in the number of male and female patients, despite more male patients being observed in adolescence. Both marital and occupational status were negatively affected by the disease. The number of patients admitted each year revealed an increasing trend in general. Regarding deep-seated tumors, 77.6% occurred in the head and neck region, and 3.8% were NF1-associated MPNSTs. Almost a quarter of patients were re-admitted after the first admission, and the mean re-admission time interval was 1.5 years. In summary, we developed the largest multidisciplinary NF1 healthcare center in China, which enables Chinese NF1 patients to access more appropriate healthcare, thereby alleviating the socioeconomic burden of disease among patients.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".