Assessing the knowledge of endometriosis diagnostic tools in a large, international lay population: an online survey
Bibliographic record
Abstract
OBJECTIVE: To assess the general population's knowledge regarding the utility and availability of tools to diagnosis endometriosis, with a focus on ultrasound. DESIGN: An international cross-sectional online survey study was performed between August and October 2019. SETTING AND POPULATION: 5301 respondents, representing 73 countries. METHODS: In all, 23 questions survey focused on knowledge of endometriosis diagnosis distributed globally via patient- and community-endometriosis groups using social media. MAIN OUTCOMES AND MEASURES: Descriptive data of the knowledge of diagnostic tools for diagnosing endometriosis, including details about diagnosis using ultrasound. RESULTS: In all, 84.0% of respondents had been previously diagnosed with endometriosis, 71.5% of whom had been diagnosed at the time of surgery. Ultrasound and MRI were the methods of diagnosis in 6.5% and 1.8%, respectively. A total of 91.8%, 28.8% and 16.6% of respondents believed surgery, ultrasound and MRI could diagnose endometriosis, respectively (more than one answer allowed). In those diagnosed by surgery, 21.7% knew about ultrasound as a diagnosis method, whereas in those diagnosed non-surgically, 51.5% knew (P < 0.001). In all, 14.7%, 31.1% and 18.2% stated superficial, ovarian and deep endometriosis could be diagnosed with ultrasound (32.9% stated they did not know which phenotypes of endometriosis could be diagnosed). Lastly, 58.4% of respondents do not believe they could access an advanced ultrasound in their region. CONCLUSIONS: There is a limited appreciation for the role of non-surgical diagnostic tests for endometriosis among lay respondents to this survey. TWEETABLE ABSTRACT: International survey shows limited awareness of lay respondents about non-surgical endometriosis diagnostic tools.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".