Quality of life in patients with neurofibromatosis type 1 and 2 in Canada
Bibliographic record
Abstract
Abstract Background There is scarce data on the quality of life of people with neurofibromatosis type 1 (NF1) and type 2 (NF2) in Canada. Methods A cross-sectional study of adults with NF1 and NF2 attending a tertiary center. Patients completed generic measures (SF-36, EQ-5D-5L, and PROMIS pain interference) and disease-specific questionnaires (PedsQL NF1 module and the NFTI-QOL for NF2). We compared generic scores between NF1 and NF2 individuals and used regression models to assess factors associated with quality of life. Results Hundred and eighty-four participants were enrolled. Mean age was 33 years in NF1 and 40 years in NF2. NF1 and NF2 individuals had lower employment rates and lower scores in all domains of the SF-36 compared to the general Canadian population (P < .005). Using the EQ-5D-5L, there was a high proportion of pain (64% in NF1 and 74% in NF2) and anxiety/depression (60% in NF1 and 68% in NF2). Pain interference correlated with poor quality of life in NF1 and NF2; perceived physical appearance was the main predictor of mental well-being in NF1. Conclusions Individuals with NF1 and NF2 have low quality of life, and this correlates with pain, anxiety, and depression, which are prevalent in NF1 and NF2. Perceived physical appearance predicts quality of life in NF1. A multidisciplinary approach is necessary for patients with NF1 and NF2, including mental health and pain management.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".