A National Modified Delphi Consensus on the Referral and Management of NF1 Plexiform Neurofibroma
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Neurofibromatosis type 1 (NF1) is a genetic disorder. Up to 50% of NF1 patients develop plexiform neurofibromas (PN). Despite revisions in diagnostic standards, there remains a lack of consensus on referral, treatment, monitoring and transition processes for NF1-PN. The study aimed to establish a Canada-wide consensus on the best practice for referral and management of patients with NF1-PN to help generate guidance where evidence on the long-term use of MEK inhibitors is lacking. METHODS: The study used a modified Delphi method. The steering committee (SC) identified 4 topics of focus and developed 44 consensus statements. Following ratification, 43 statements were developed into an online survey sent to 113 healthcare practitioners (HCPs) involved in NF1-PN management across Canada. Respondents used a 4-point Likert scale to indicate agreement with each statement. The threshold for consensus agreement was 75%. RESULTS: A total of 56 responses were received, predominantly from Ontario. Most respondents were neuro-oncologists (34%) and had over 11 years of experience (57%). Consensus was reached on 41 of 43 statements (95%), enabling the SC to develop recommendations for NF1-PN patient care and a treatment algorithm outlining key timings for treatment and management. CONCLUSIONS: To our knowledge, this is the first national Delphi consensus on NF1-PN. Strong agreement was seen from HCPs on critical timings in NF1-PN treatment and management. The proposed recommendations and treatment algorithm provide a framework to enhance patient care and support ongoing research into optimizing care for NF1-PN patients, not just in Canada but globally.
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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 | 0.202 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".