Bibliographic record
Abstract
We appreciate the feedback from Prof. Guerriero and colleagues and would like to take the opportunity to address their concerns. We agree that the International Deep Endometriosis Analysis (IDEA) consensus opinion1 has contributed significantly to advancing ultrasound for endometriosis. Transvaginal ultrasound for endometriosis (eTVUS) is performed to investigate diagnostically for endometriosis1 and/or map endometriosis preoperatively for surgery2. For the latter, as detailed in our original manuscript3, we believe unequivocally that an IDEA-guided comprehensive eTVUS should always be performed. From years of teaching IDEA-guided eTVUS, we have seen how challenging it is, particularly for general imaging centers with lower volumes of gynecological scans and endometriosis cases, compared with those in specialized or tertiary-level centers. Whilst it would be ideal for everyone to perform comprehensive IDEA-guided eTVUS, we now believe this is currently, and will remain, unattainable. Our simplified approach does not intend to replace the IDEA consensus opinion, but rather to use its ‘common language’ to offer an alternative in non-expert centers or in environments in which a comprehensive scan is not feasible, such as regional centers. Our protocol is designed to make the diagnosis of endometriosis more accessible, helping to reduce diagnostic delays and patient suffering. Regarding the concerns about omitting certain areas of the pelvis, we aimed to prioritize the regions most affected by endometriosis. Evidence shows that disease in the anterior compartment or parametrium rarely occurs without uterosacral ligament deep endometriotic nodules or pouch of Douglas obliteration. The most commonly impacted segment of the bowel – the upper rectum – is also covered by our simplified approach, which is likely sufficient for initial endometriosis diagnosis. Like Prof. Guerriero and colleagues, we acknowledge the importance of rigorous training. However, the IDEA consensus opinion involves mastering many anatomical structures that are beyond what is typically taught in gynecological ultrasound training (which generally covers just the uterus and ovaries). Our proposed protocol involves mastering less. There is no need for a learning-curve study to know that learning to do fewer things is easier than learning to do many. Furthermore, we carefully crafted this proposal to ensure that the descriptors and methodology remain consistent with the IDEA consensus opinion, to allow a smooth segue to performing comprehensive eTVUS, once proficiency of this simplified approach has been obtained. Whilst it is fair to criticize the introduction of an opinion by only two authors, the IDEA consensus opinion was similarly published prior to any supporting prospective studies. Furthermore, the IDEA pilot study, whilst showing good sensitivity for the detection of disease (88.4%), had suboptimal specificity (78.8%)2. In the context of surgical planning, this is not problematic, as overpreparation is preferable to underpreparation. However, in general screening, a low false-positive rate is crucial to avoid inaccurate diagnoses, unnecessary interventions and, potentially, harm. We appreciate the dialogue this Correspondence has sparked, and we hope it will lead to further studies that explore the balance between accessibility and thoroughness in ultrasound protocols for endometriosis. Furthermore, we look forward to working with the IDEA team in the future as these techniques evolve further.
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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 | 0.005 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.054 | 0.042 |
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".