Assessment of breast lesions using Doppler with contrast agents.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the use of contrast agents on ultrasound examination of breast lesions and to analyse the capacity of this technique to achieve a differential diagnosis of benign and malignant lesions, using as a pattern a histological study of the lesions. MATERIALS AND METHODS: Seventy-two women with suspected malignant breast lesions participated in this randomised prospective study, undergoing a colour Doppler ultrasound (US). The results of the study were measured before and after the use of US contrast agents and were compared with the ones obtained from the histological study of the pieces. RESULTS: Malignant breast tumours showed, when using US Doppler with contrast agent, a hypervascularity pattern in 78.1% of the cases. The predominant pattern in benign tumours was avascular (11 cases, 64.7%). The intensity of the signal in the first minute was intense in 43.7% or moderate in 40.6% of the cases with malignant tumours versus the benign tumours where no signal was registered in 64.7% of the cases. A marked increase in the sensitivity and the predictive negative value of the ultrasound signal was noted with the use of potentiating substances. CONCLUSIONS: The use of contrast agents in colour Doppler US studies improve the differential diagnosis of benign and malignant breast lesions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".