Creation of an international registry to support discovery in schwannomatosis
Bibliographic record
Abstract
Schwannomatosis is a tumor suppressor syndrome that causes multiple tumors along peripheral nerves. Formal diagnostic criteria were first published in 2005. Variability in clinical presentation and a relative lack of awareness of the syndrome have contributed to difficulty recognizing affected individuals and accurately describing the natural history of the disorder. Many critical questions such as the mutations underlying schwannomatosis, genotype-phenotype correlations, inheritance patterns, pathologic diagnosis of schwannomatosis-associated schwannomas, tumor burden in schwannomatosis, the incidence of malignancy, and the effectiveness of current, or new treatments remain unanswered. A well-curated registry of schwannomatosis patients is needed to facilitate research in field. An international consortium of clinicians and scientists across multiple disciplines with expertise in schwannomatosis was established and charged with the task of designing and populating a schwannomatosis patient registry. The International Schwannomatosis Registry (ISR) was built around key data points that allow confirmation of the diagnosis and identification of potential research subjects to advance research to further the knowledge base for schwannomatosis. A registry with 389 participants enrolled to date has been established. Twenty-three additional subjects are pending review. A formal process has been established for scientific investigators to propose research projects, identify eligible subjects, and seek collaborators from ISR sites. Research collaborations have been created using the information collected by the registry and are currently being conducted. The ISR is a platform from which multiple research endeavors can be launched, facilitating connections between affected individuals interested in participating in research and researchers actively investigating a variety of aspects of schwannomatosis. © 2016 Wiley Periodicals, Inc.
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 | 0.106 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".