T2-Hypointense Adnexal Lesions: An Imaging Algorithm
Bibliographic record
Abstract
T2-weighted sequences are an integral part of magnetic resonance (MR) imaging performed for the characterization of adnexal lesions. A relatively small number of these lesions demonstrate low signal intensity on T2-weighted MR images. In the majority of cases, a specific diagnosis can be made by interpreting the signal intensity of the lesion with respect to certain pathologic correlates, including blood products, smooth muscle, fibrous tissue, and calcification, as well as high lesion cellularity. For example, lesions that are at least as dark as skeletal muscle are almost always benign, whereas those whose T2 signal intensity is higher than that of skeletal muscle constitute a more heterogeneous group composed of benign, borderline, and malignant disease entities. The authors propose a diagnostic algorithm that takes these features into account, as well as the appearances of the lesion with additional pulse sequences, to aid in the correct interpretation of T2-hypointense adnexal lesions. Knowledge of the anatomy, the T1-weighted imaging features, and the enhancement characteristics of adnexal lesions allows accurate characterization of these lesions, resulting in appropriate patient management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".