Strengths and limitations of diagnostic tools for endometriosis and relevance in diagnostic test accuracy research
Bibliographic record
Abstract
Endometriosis is a chronic systemic disease that can cause pain, infertility and reduced quality of life. Diagnosing endometriosis remains challenging, which yields diagnostic delays for patients. Research on diagnostic test accuracy in endometriosis can be difficult due to verification bias, as not all patients with endometriosis undergo definitive diagnostic testing. The purpose of this State-of-the-Art Review is to provide a comprehensive update on the strengths and limitations of the diagnostic modalities used in endometriosis and discuss the relevance of diagnostic test accuracy research pertaining to each. We performed a comprehensive literature review of the following methods: clinical assessment including history and physical examination, biomarkers, diagnostic imaging, surgical diagnosis and histopathology. Our review suggests that, although non-invasive diagnostic methods, such as clinical assessment, ultrasound and magnetic resonance imaging, do not yet qualify formally as replacement tests for surgery in diagnosing all subtypes of endometriosis, they are likely to be appropriate for advanced stages of endometriosis. We also demonstrate in our review that all methods have strengths and limitations, leading to our conclusion that there should not be a single gold-standard diagnostic method for endometriosis, but rather, multiple accepted diagnostic methods appropriate for different circumstances. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.166 | 0.450 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".