EP24.31: Individuals' preferences for endometriosis diagnosis tests: focus group discussions with individuals diagnosed with endometriosis
Bibliographic record
Abstract
Endometriosis affects approximately 10% of reproductive-age individuals and is diagnosed through methods including anamnesis, physical examination, imaging, biomarkers, and surgery with histological assessment. As we move toward a medical culture that prioritises autonomy and patient involvement, we must consider how these principles are applicable to the diagnostic process for endometriosis. This study hopes to understand and identify existing patient preferences for endometriosis diagnosis to promote best clinical approaches and patient comfort. The development of individuals' preferences for endometriosis diagnosis tests IPEDT comprises four steps. This study concentrated on the initial step, which entailed conducting semi-structured focus group discussions with individuals diagnosed with endometriosis to ascertain their perspectives on the diagnosis process. The analysis of transcripts followed grounded theory methodology, with two authors independently coding the data. These codes were then organised into categories and themes to reflect participant perspectives. We conducted two focus groups with seven individuals, aged 26 to 56, all diagnosed with endometriosis. Through these discussions, we identified five key themes perceived as important by participants when selecting a diagnostic method for endometriosis: 1) expertise of healthcare provider (7/7 participants), 2) patient-physician rapport (7/7 participants), 3) visualization of endometriosis (3/7 participants), 4) accuracy of diagnostic test (3/7 participants), and 5) risk of complications (2/7 participants). The unique insights garnered from this study helped to further our understanding of patient preferences regarding the diagnosis of endometriosis and the level of awareness patients have about the different diagnostic methods used to diagnose endometriosis. These findings will help guide the next step of the study which involves a discreet choice experiment and will help to educate physicians in best practices to follow in their clinics based on the patient experience.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".