A UK wide research registry for inherited peripheral neuropathy
Bibliographic record
Abstract
Charcot-Marie-Tooth disease (CMT) is one of the commonest genetic neurological diseases affecting 1 in 2500 people). We have established a UK wide database for patients with CMT who are willing to be contacted for ethically approved research projects. Up until now there has been no such database for individuals with CMT in the UK representing a major barrier for recruiting to clinical trials for this rare group of diseases. The UK CMT database is hosted in the secure UCL ‘safe data haven’ and has undergone favourable ethical review (IRAS 319927). Any individual with CMT resident in the UK can register on the database via the following weblink https://redcap.idhs.ucl.ac.uk/surveys/?s=DN44N9HM84NH7XN7. This means that other than directing interested and eligible patients with CMT to this webpage, the clinician is not required to consent or enter data for the participant. A part time database manager will be employed to ensure the accuracy of the entered demographic and clinical data. Clinicians caring for database participants may therefore be contacted by the database manager to corroborate clinical details and in particular the type of genetic variant.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.020 |
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".