P.034 Understanding and implementing multidisciplinary care for patients with neurofibromatosis 1 in British Columbia
Bibliographic record
Abstract
Background: Neurofibromatosis 1 (NF1) is a multisystem neurocutaneous disorder. Treatment involves multiple specialists. There are currently no multidisciplinary clinics for adults with NF1 in BC, which impacts communication between subspecialties. We sought perspectives of patients and providers to identify the impact of and solutions to gaps in care. Methods: Focus groups with patients (2 groups; 9 patients) and physicians (10) who see people with NF1 were conducted. Thematic content analysis was applied to the data to derive major themes. Concurrently, quarterly NF multidisciplinary rounds were initiated to enhance coordination of care. Results: Major themes emerged around the need for increased coordination and communication amongst providers. Specifically, physicians identified working in “siloed care structures”, and patients and providers identified lack of awareness of expertise and barriers to accessing care. Conclusions: Focus groups enable inclusion of patient and provider perspectives in developing solutions to gaps in care. The importance of supporting interdisciplinary communication in caring for NF1 patients was confirmed in focus groups. To date, we have held multidisciplinary NF rounds, with 12 cases discussed. Disciplines represented include neurology, pediatrics, radiology, neuro-ophthalmology, neuro-otology, pathology, orthopedic plastic and neurosurgery, medical and radiation oncology, and the hereditary cancer program. Telehealth format enables participation from distributed centres across BC.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".