The Development and Validation of a Patient Questionnaire Tool for the Assessment of Patient-Reported Experiences With Endometriosis Ultrasound
Bibliographic record
Abstract
OBJECTIVES: Endometriosis ultrasound is an accurate, cost-effective, and non-invasive diagnostic tool that can help improve the diagnostic delay that patients with endometriosis experience. As an emerging diagnostic method, the perspectives of patients undergoing endometriosis ultrasound remain unexplored. Therefore, the objective of this study was to develop and validate an assessment tool that evaluates patient-reported experiences with endometriosis ultrasound as a decision-making tool. METHODS: This was a 2-part study with the first phase involving a modified Delphi consensus process including a panel of clinicians, sonologists, researchers, and a patient with lived experience of endometriosis. Pre- and post-ultrasound patient questionnaires were subsequently developed. The second phase included validating the questionnaires via a prospective cross-sectional survey study carried out at the Endometriosis Clinic at McMaster University Medical Centre in Hamilton, Canada. Data were analyzed using measures of central tendency, chi-square test, and Fisher exact test as appropriate. Statistical significance was determined by 2-sided P values less than 0.05. RESULTS: Pre- and post-ultrasound questionnaires were developed comprising 8 and 10 questions, respectively. Of 46 respondents, there was a similar representation of patients with an abnormal endometriosis ultrasound (58.7% of patients, n = 27) and those with a normal endometriosis ultrasound (41.3 %, n = 19). Moreover, endometriosis ultrasound results helped most participants (84.8%, n = 39) with treatment decision-making. CONCLUSIONS: This study validates a survey tool that can be used clinically to assess patient-reported experiences with endometriosis ultrasound. It also demonstrates the highly informative nature of endometriosis ultrasound, with many patients choosing to defer more invasive diagnostic methods (i.e., surgery).
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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 | 0.057 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".