Non-Breast Implantable Medical Devices and Associated Malignancies: A Systematic Review
Bibliographic record
Abstract
Innovation in healthcare has led to the development of numerous implantable medical devices (IMDs). However, advances in our knowledge of breast implant-associated malignancies have raised questions about the prevalence, etiology, and management of malignancies associated with non-breast IMDs. The objective of this study was to examine the prevalence and characteristics of malignancies associated with non-breast IMDs. An expert medical librarian developed the search strategy for this review. Databases included MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. In addition, gray literature sources were searched, and relevant references from systematic reviews and meta-analyses were included. The PRISMA guideline was followed for the review. Risk of bias was evaluated with the JBI Critical Appraisal tools. A total of 12,230 articles were reviewed, with a total of 77 meeting inclusion criteria. Risk of bias was highest with case reports (moderate, average of 65.1% with range of 37.5% to 100%) and low for the remaining study types. In total, 616 cases of IMD-associated malignancies were identified. Malignancies associated with IMDs were reported in the head and neck (543, 88.1%), lower extremity (57, 9.6%), thorax (9, 1.4%), abdomen (3, 0.5%), and genitourinary system (2, 0.3%). The most common malignancy type in the lower extremity was sarcoma, in the head and neck was squamous cell carcinoma, and in the thorax was lymphoma. This study is the first comprehensive systematic review of its kind. Overall, the oncologic risk of IMDs is low. The discussion of malignancy is an important part of the overall consent process, and malignancy should be considered with any new signs or symptoms in the anatomic area of an implant. More data are needed to better understand how primary malignancies occur around IMDs and how to reduce this risk.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".