Acoustic streaming cannot discriminate reliably between endometriomas and other types of adnexal lesion: a multicenter study of 633 adnexal masses
Bibliographic record
Abstract
OBJECTIVE: To determine the ability of acoustic streaming to discriminate between endometriomas and other adnexal masses. METHODS: We used data from 1938 patients with an adnexal mass included in Phase 2 of the International Ovarian Tumor Analysis (IOTA) study. All patients had been examined by transvaginal gray-scale and Doppler ultrasound following a standardized research protocol. Assessment of acoustic streaming was voluntary and was carried out only in lesions containing echogenic cyst fluid. Acoustic streaming was defined as movement of particles inside the cyst fluid during gray-scale and/or color Doppler examination provided that the probe had been held still for two seconds to ensure that the movement of the particles was not caused by movement of the probe or the patient. Only centers where acoustic streaming had been evaluated in > 90% of cases were included. Sensitivity, specificity, positive and negative likelihood ratios (LR+, LR-), and positive and negative predictive values (PPV and NPV) of acoustic streaming with regard to endometrioma were calculated. RESULTS: 460 (24%) masses were excluded because they were examined in centers where </= 90% of the masses with echogenic cyst fluid had been evaluated for the presence of acoustic streaming. Acoustic streaming was evaluated in 633 of 646 lesions containing echogenic cyst fluid. It was present in 19 (9%) of 209 endometriomas and in 55 (13%) of 424 other lesions. This corresponds to a sensitivity of absent acoustic streaming with regard to endometrioma of 91% (190/209), a specificity of 13% (55/424), LR+ of 1.04, LR- of 0.69, PPV of 34% (190/559) and NPV of 74% (55/74). CONCLUSIONS: Acoustic streaming cannot discriminate reliably between endometriomas and other adnexal lesions, and the presence of acoustic streaming does not exclude an endometrioma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".