The Impact of Implant Location on Breast Cancer Characteristics in Previously Augmented Patients: A Systematic Literature Analysis
Bibliographic record
Abstract
Background: There is a paucity of data comparing the oncologic properties of breast cancer among patients previously having undergone breast augmentation in either the subglandular or subpectoral planes. The objective of the present systematic review was to evaluate whether implant location influenced the characteristics of breast tumors in previously augmented women. Methods: A systematic literature search was performed to identify relevant articles reporting tumor characteristics in augmented patients. The search included published articles in three electronic databases; Ovid MEDLINE, EMBASE, and PubMed. Comparative studies (subglandular vs. subpectoral) were included. Results: Analysis of data pooled from the included studies showed that subglandular implants had a higher frequency of tumors between 2 to 5 cm (26.5% vs. 9.9%, P = 0.0130). Subglandular implants also had a higher frequency of stage 2 tumors (42.9% vs. 23.7%, P = 0.0308). There was no significant difference in lymphovascular invasion between the 2 groups. These results of this systematic review suggest that the prognosis of patients undergoing augmentation is unaffected by implant location (subpectoral vs. subglandular). Conclusions: With the absence of large randomized controlled trials, our study provides surgeons with an evidence-based reference to improve informed consent with regards to implant placement.
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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".