Societal costs and quality of life associated with arginase 1 deficiency in a European setting – a multinational, cross-sectional survey
Bibliographic record
Abstract
BACKGROUND AND AIMS: Arginase 1 deficiency (ARG1-D) is a ultrarare disease with manifestations that cause mobility and cognitive impairment that progress over time and may lead to early mortality. Diseases such as ARG1-D have a major impact also outside of the health care sector and the aim of this study was to estimate the current burden of disease associated with ARG1-D from a societal perspective. METHODS: The study was performed as a web-based survey of patients with ARG1-D and their caregivers in four European countries (France, Portugal, Spain, United Kingdom). The survey was distributed at participating clinics and included questions on e.g. symptoms (including the Gross Motor Function Classification System, GMFCS, and cognitive impairment), health care use, medication, ability to work, caregiving, and impact on health-related quality-of-life (HRQoL) using the EQ-5D-5L. RESULTS: The estimated total mean societal cost per patient and year was £63,775 (SD: £49,944). The cost varied significantly with both mobility impairment (from £49,809 for GMFCS level 1 to £103,639 for GMFCS levels 3-5) and cognitive impairment (from £43,860 for mild level to £99,162 for severe level). The mean utility score on the EQ-5D-5L for patients was 0.498 (SD: 0.352). The utility score also varied significantly with both mobility impairment (from 0.783 for GMFCS level 1 to 0.153 for GMFCS level 3-5) and cognitive impairment (from 0.738 for mild level to 0.364 for severe level). CONCLUSIONS: Similar to other studies of rare diseases, the study is based on a limited number of observations. However, the sample appear to be reasonably representative when comparing to previous studies of ARG1-D. This study shows that ARG1-D is associated with a high societal cost and significant impact on HRQoL. Earlier diagnosis and better treatment options that can postpone or withhold progression may therefore have a potential for improved HRQoL and savings for the patient, caregiver, and society.
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 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 | 0.005 | 0.002 |
| 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".