Development of a visual, patient-reported tool for assessing the multi-dimensional burden of endometriosis
Bibliographic record
Abstract
OBJECTIVE: Inadequate communication about endometriosis symptom burden between women and healthcare providers is a barrier for optimal treatment. This study describes the development of the EndoWheel, a patient-reported assessment tool that visualizes the multi-dimensional burden of endometriosis to facilitate patient-provider communication. METHODS: Assessment questions for the tool were developed using an iterative Delphi consensus process. A consensus phase included additional practitioners and specialists to broaden perspectives and select revised statements. Semi-structured qualitative interviews were conducted with 13 women with endometriosis to assess the scoring and content of the measures. RESULTS: Symptoms included in the tool were pelvic pain, vaginal bleeding, bowel/bladder symptoms, energy levels, fertility, impact on activities, emotional and sexual well-being, and self-perceived global health. Additional life impact areas included relationships, social and occupational activity, and self-perception. The 13 interviewees completed the tool in approximately 5-6 min (range 4.0-7.5 min). Most participants (92%) perceived that the tool would enable better patient-provider communication, including addressing symptoms and areas of impact not normally discussed during office visits. CONCLUSION: Similar to visual circular tools used in burden assessment of other chronic diseases, the tool may facilitate improved patient dialogue with providers around endometriosis treatment goals and options.
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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".