Breast Reconstruction: Review of Surgical Methods and Spectrum of Imaging Findings
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
Breast reconstruction after mastectomy is often requested by women with breast cancer who are ineligible for breast-conserving therapy and women with a high genetic risk for breast cancer. Current breast reconstruction techniques are diverse and may involve the use of an autologous tissue flap, a prosthetic implant, or both. Regardless of the technique used, cancer may recur in the reconstructed breast; in addition, in breasts reconstructed with autologous tissue flaps, benign complications such as fat necrosis may occur. To detect breast cancer recurrences at a smaller size than can be appreciated clinically and as early as possible without evidence of metastasis, radiologists must be familiar with the range of normal and abnormal imaging appearances of reconstructed breasts, including features of benign complications as well as those of malignant change. Images representing this spectrum of findings were selected from the clinical records of 119 women who underwent breast magnetic resonance (MR) imaging at the authors' institution between January 2009 and March 2011, after mastectomy and breast reconstruction. In 32 of 37 women with abnormal findings on MR images, only benign changes were found at further diagnostic workup; in the other five, recurrent breast cancer was found at biopsy. Four of the five had been treated initially for invasive carcinoma, and one, for multifocal ductal carcinoma; three of the five were carriers of a BRCA gene mutation. On the basis of these results, the authors suggest that systematic follow-up examinations with breast MR imaging may benefit women with a reconstructed breast and a high risk for breast cancer recurrence.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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