A Novel Level I Oncoplastic Surgery Technique for Tumors Located in UIQ of the Breast Far from the Nipple: The “Cross” Technique
Bibliographic record
Abstract
Breast surgery was revolutionized with the use of oncoplastic reshaping techniques minimizing breast deformities and esthetic complications. However, the application of the current oncoplastic techniques becomes challenging in some situations such as small-size breasts and when the tumors are located in special locations of the breast, for example, upper inner quadrant. In this article, an optimized oncoplastic technique named the "Cross" technique is introduced to overcome the abovementioned problems in the surgery of breast tumors located in the upper inner quadrant far from the center of the breast. Nineteen oncoplastic surgeries were performed by the same breast surgeon. The mean diameter and weight of the excised specimens were 20 mm and 74 g. The mean age of the patients was 51 years. Clear surgical margins were obtained in all patients. There was no marked deformity in the breast after surgery. The optimized technique produced promising results in our hands when applied to a selected group of patients. Moreover, the technique was found to reduce the need for revision surgery in ptotic breasts, as the alteration in the shape of the breast undergoing surgery is not significant enough to introduce asymmetry to the breasts.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".