Assessing the Utility of a Patient-Facing Diagnostic Tool Among Individuals With Hypermobile Ehlers-Danlos Syndrome: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Hypermobile Ehlers-Danlos syndrome (hEDS), characterized by joint hypermobility, skin laxity, and tissue fragility, is thought to be the most common inherited connective tissue disorder, with millions affected worldwide. Diagnosing this condition remains a challenge that can impact quality of life for individuals with hEDS. Many with hEDS describe extended diagnostic odysseys involving exorbitant time and monetary investment. This delay is due to the complexity of diagnosis, symptom overlap with other conditions, and limited access to providers. Many primary care providers are unfamiliar with hEDS, compounded by genetics clinics that do not accept referrals for hEDS evaluation and long waits for genetics clinics that do evaluate for hEDS, leaving patients without sufficient options. OBJECTIVE: This study explored the user experience, quality, and utility of a prototype of a patient-facing diagnostic tool intended to support clinician diagnosis for individuals with symptoms of hEDS. The questions included within the prototype are aligned with the 2017 international classification of Ehlers-Danlos syndromes. This study explored how this tool may help patients communicate information about hEDS to their physicians, influencing the diagnosis of hEDS and affecting patient experience. METHODS: Participants clinically diagnosed with hEDS were recruited from either a medical center or private groups on a social media platform. Interested participants provided verbal consent, completed questionnaires about their diagnosis, and were invited to join an internet-based focus group to share their thoughts and opinions on a diagnostic tool prototype. Participants were invited to complete the Mobile App Rating Scale (MARS) to evaluate their experience viewing the diagnostic tool. The MARS is a framework for evaluating mobile health apps across 4 dimensions: engagement, functionality, esthetics, and information quality. Qualitative data were analyzed using affinity mapping to organize information and inductively create themes that were categorized within the MARS framework dimensions to help identify strengths and weaknesses of the diagnostic tool prototype. RESULTS: In total, 15 individuals participated in the internet-based focus groups; 3 (20%) completed the MARS. Through affinity diagramming, 2 main categories of responses were identified, including responses related to the user interface and responses related to the application of the tool. Each category included several themes and subthemes that mapped well to the 4 MARS dimensions. The analysis showed that the tool held value and utility among the participants diagnosed with hEDS. The shareable ending summary sheet provided by the tool stood out as a strength for facilitating communication between patient and provider during the diagnostic evaluation. CONCLUSIONS: The results provide insights on the perceived utility and value of the tool, including preferred phrasing, layout and design preferences, and tool accessibility. The participants expressed that the tool may improve the hEDS diagnostic odyssey and help educate providers about the diagnostic process.
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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".