P.138 A systematic review of the incidence and prevalence of Neurofibromatosis type 1 and 2
Bibliographic record
Abstract
Background: Neurofibromatosis 1 and 2 (NF1 and NF2) are autosomal dominant genetic disorders caused by mutations in tumour suppressor genes. Methods: We conducted a systematic review of the incidence and prevalence of NF1 and NF2 in OVID Medline, OVID Embase, Web of Science, and Cinahl. We included studies until February 19, 2021, that identified cases based on established criteria. Studies were appraised for quality using the Joanna Briggs Institute Prevalence Critical Appraisal tool. Pooled incidence and prevalence rates were estimated through meta-analysis. Results: Of 1,936 studies, 1,866 were irrelevant after title and abstract screening. Sixteen of 69 studies with full text assessment were included for full review: 13 regarding NF1 and 6 regarding NF2. Incidence rates for NF1 and NF2 ranged from 1/11,494 to 1/1,871 and 1/62,185 to 1/33,000 respectively. Prevalence rates for NF1 and NF2 ranged from 1/6,238 to 1/1,001 and 1/600,000 to 1/56,161 respectively. Meta-analysis will be presented at the conference. Conclusions: An accurate estimate of the incidence and prevalence of NF1 and NF2 will offer more insight into health resource allocation. Increased funding and resources for the development of early diagnostic and treatment tools for NF1 and NF2 may improve the quality of life of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".