Breast Abscesses: Evidence-based Algorithms for Diagnosis, Management, and Follow-up
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 it