Patient reported outcomes in Usher Syndrome: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Usher Syndrome (USH) is a leading cause of deaf-blindness and significantly impacts quality of life. With no cure, it is essential to focus on addressing functional impairments and emotional well-being in 10 affected individuals. METHODS: A systematic search was conducted on MEDLINE, Embase, PsychInfo, CINHAL, Web of Science, and Cochrane Library until 4 September 2024 to identify studies on patient-reported outcomes (PROs) in USH. RESULTS: 27 studies (1,009 participants, mean age 47.0, 52.4% female) focused on USH, with 74.1% having type 2, 31.4% having type 1, and 6.8% having type 3. 18 studies used quantitative methods, and 9 were qualitative. The Glasgow Benefit Inventory (GBI) was the most common PRO measure, followed by the Nijmegen Cochlear Implant Questionnaire, Usher Lifestyle Survey (ULS), and SF-12 (2 studies each). Weighted GBI scores indicated moderate benefits, but lower physical scores highlighted ongoing limitations. The ULS found that participants needed equipment for information access and mobility assistance. Notably, no studies addressed vision-related interventions, and only one used a vision-specific PRO measure. Qualitative findings emphasized psychological well-being and social support. DISCUSSION: PRO data in USH is limited, underscoring the need for standardized measures and vision-related interventions. Ongoing challenges emphasize the need for multidisciplinary approaches to improve quality of life.
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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".