QOL-06. OPTIMIZING NEUROFIBROMATOSIS CARE: A COMPREHENSIVE ECONOMIC EVALUATION OF A SPECIALIZED EXPERTISE CENTER’S IMPACT ON PATIENT OUTCOMES
Bibliographic record
Abstract
Abstract Neurofibromatosis type 1 (NF1), NF2-related schwannomatosis (NF2) and other schwannomatoses represent a spectrum of genetic disorders characterized by the development of tumors on nerve tissues, profoundly impacting patients’ quality of life and posing complex challenges. Recognizing the imperative for comprehensive care delivery, we undertook to implement a Neurofibromatosis Expertise Center (CE) at the Centre hospitalier de l’Université de Montréal (CHUM), Quebec, Canada. Employing a robust analysis framework, we aimed to assess the economic viability of the CE by quantifying the Quality-Adjusted Life Years (QALYs) gained through centralized multidisciplinary neurofibromatosis care (cost-utility ratio = CAD/QALY gained). Leveraging data from validated questionnaires including the SF-6D, our preliminary analysis revealed significant enhancements in patients’ quality of life across diverse domains, including physical pain, visible cutaneous lesions, cognitive dysfunction, hearing loss, malignancies, psychiatric comorbidities, unemployment, physical disability, and social isolation. Notably, among the respondents (N = 115), patients receiving CE services reported a 12% increase in general health compared to a 25% decline among those without CE services. Moreover, 28% of CE patients reported improved overall health compared to 12% of those without CE services. Using the Work Productivity and Activity Impairment questionnaire (WPAI-GH), we found that cohorts followed in CE for ≥ 1 year (N = 45) experienced less work productivity losses compared to the most recent cohort (N = 32), which presented scores exceeding 6 on a 1-to-10 scale. We hypothesize that the establishment of the CE will yield profound improvements in patient health outcomes and alleviate financial strains on the Ministère de la Santé et des Services Sociaux du Québec (MSSS). Therefore, our next steps include discussing a strategy for funding application to the MSSS to ensure the accessibility of necessary care for patients. Continuing data collection and analysis will also provide a more comprehensive overview of patient characteristics and needs.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".