Breast Abscesses: Evidence-based Algorithms for Diagnosis, Management, and Follow-up
Bibliographic record
Abstract
Radiologists who regularly perform breast ultrasonography will likely encounter patients with breast abscesses. Although the traditional approach of surgical incision and drainage is no longer the recommended treatment, there are no clear guidelines for management of this clinical condition. Breast abscesses that develop in the puerperal period generally have a better course than nonpuerperal abscesses, which tend to be associated with longer treatment times and a higher rate of recurrence. The available literature on treatment of breast abscesses is imperfect, with no clear consensus on drainage, antibiotic therapy, and follow-up. By synthesizing the data available from studies published in the past 20 years, an evidence-based algorithm for management of breast abscesses has been developed. The proposed algorithm is easy to follow and has been validated by a multidisciplinary team approach and applied successfully during the past 2 years. Breast abscesses are a challenging clinical condition, and radiologists have a pivotal role in evaluation and follow-up of these 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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".