Bone Research Society Abstracts 2020
Bibliographic record
Abstract
Introduction: Alkaptonuria (AKU) is an iconic autosomal recessive severe multisystem disorder of the tyrosine degradation pathway due to lack of homogentisate dioxygenase resulting in increased circulating and urinary homogentisic acid (HGA), with aortic valve disease and spondyloarthropathy dominating the morbidity.Nitisinone, an inhibitor of hydroxyphenylpyruvate dioxygenase, markedly decreases HGA production and has been used off-label in the National Alkaptonuria Centre (NAC) in Liverpool.Patients and Methods: A total of 72 AKU patients visited Royal Liverpool University Hospital between 2007 and 2018.CT Bone mineral density was carried out to assess fracture risk.Systematic data was collected on fractures as well as ruptures of tendons, muscle and ligament.Data was also collected on joint replacements by questionnaire, and joint involvement by whole body F18PETCT scan.Echocardiogram was carried out to assess aortic valve disease.Off-label nitisinone 2mg daily was administered to English and Scottish patients attending the NAC.Morbidity was also assessed using a composite score, the Alkaptonuria Severity Score Index (AKUSSI).The data on the use of nitisinone for 6 years was analysed, in these 72 patients attending annually.Results: The mean (SD) age was female (52.3+15.8years) and male (48.5+14.9years) .Osteopenia was present in 69.6%.In the cohort there were 57 fractures, 59 ruptures and 109 joint replacements.Knees, hips and shoulders were commonly affected.58.3% of patients had echocardiographic proven aortic valve disease.The change in AKUSSI was significantly lower at six years post-nitisinone (p<0.0001).Conclusion: This large cohort of a rare osteoarticular disease, AKU, was systematically characterized and the considerable morbidity, especially relating to bone and joints is described in this dataset.Nitisinone has shown a beneficial effect in decreasing the progression of osteoarticular morbidity in this disease for the first time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.637 | 0.380 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".