P.071 Improving care for patients with neurofibromatosis 1 in British Columbia
Bibliographic record
Abstract
Background: Neurofibromatosis 1 is a multisystem, neurocutaneous disorder with a predisposition for various malignancies. There is no established care pathway or multidisciplinary clinic for adult patients with NF1 in British Columbia (BC). Patients may miss timely screening or therapeutic interventions. The development of new therapies for NF1 highlights the urgency for coordinated care. Methods: A review of existing programs and guidelines was conducted. The estimated population with NF1 in BC was determined. A working group consisting of neuromuscular neurology, pediatric neuro-oncology, adult neuro-oncology, and medical genetics identified gaps in care. Results: Approximately 2200 adult individuals with NF1 are estimated to live in BC. A three-prong approach to address identified gaps was developed: A quarterly multidisciplinary NF Case Conference was initiated. The initial session was attended by 18 providers. Focus groups for patients and providers to enhance understanding of both perspectives are being conducted. Informed by the focus groups, an NF1 Care Pathway for BC will be developed. Conclusions: Advances in treatment for NF1 prompted the formation of the BC NF Working Group to develop a strategy to improve longitudinal, multidisciplinary care. The development of a care pathway, with patient input, will improve care coordination and access to care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".