Identification of alkaptonuria in the general population: a United Kingdom experience describing the challenges, possible solutions and persistent barriers
Bibliographic record
Abstract
Progress in research into rare diseases is challenging. This paper discusses strategies to identify individuals with the rare genetic disease alkaptonuria (AKU) within the general population. Strategies used included a questionnaire survey of general practitioners, a dedicated website and patient network contact, targeted family screening and medical conference targeting. Primary care physicians of the UK were targeted by a postal survey that involved mailing 11,151 UK GPs; the response rate was 18.2%. We have identified 75 patients in the UK with AKU by the following means: postal survey (23), targeted family screening (11), patient networks and the website (41). Targeting medical conferences (AKU, rare diseases, rheumatology, clinical biochemistry, orthopaedics, general practitioners) did not lead to new identification in the UK but helped identify overseas cases. We are now aware of 626 patients worldwide including newly identified non-UK people with AKU in the following areas: Slovakia (208), the rest of Europe (including Turkey) (79), North America (including USA and Canada) (110), and the rest of the world (154). A mechanism for identifying individuals with AKU in the general population-not just in the UK but worldwide-has been established. Knowledge of patients with AKU, both in the UK and outside, is often confined to establishing their location in a particular GP practice or association with a particular medical professional. Mere identification, however, does not always lead to full engagement for epidemiological research purposes or targeting treatment since further barriers exist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".