Breast Implant Surgery; Risks and Health Complications.
Bibliographic record
Abstract
OBJECTIVE: Understand the complications of breast implant surgery, breast implant illness (BII), breast implant-associated anaplastic large cell lymphoma (BIA-ALCL), and breast implant-associated squamous cell carcinoma (BIA-SCC). Understand why universal screening for implantable devices is a proposed addition to the nursing process and how utilizing this screening tool can enable illness-specific care in a timely manner. Current research on breast implant surgery demonstrates a risk of health complications: BII, BIA- ALCL, and BIA-SCC. Impacting patient safety worldwide, it is not standard healthcare practice to screen every patient at every healthcare encounter for the presence or absence of an implantable device or a history of an implantable device, specifically breast implant devices. However, by not doing so, nurses and other healthcare providers may miss the opportunity to identify systemic illness or cancer that could be related to breast implant devices. A lack of provider and nursing awareness and a knowledge gap regarding BII, BIA- ALCL, and BIA-SCC were also identified, and this is a significant patient safety concern. MATERIALS AND METHODS: To facilitate the research process, dependent and independent variables were identified. Internet database searches and widespread exploration of Medline, CINAHL, Google Scholar, and PubMed were conducted for peer-reviewed studies on this research topic. RESULTS: Estimates show over 300,000 women had breast implant surgery in 2019 (McKernan et al., 2021). Studies from the United States, Canada, Europe, and South America demonstrate that BII is a global phenomenon. Watad et al. (2018), in their cross-sectional study, analyzed population data from a 20-year time period that identified 24,651 women with breast implants with symptoms of BII. Lack of provider follow-up has led to underreporting of BII, BIA-ALCL, and BIA-SCC cases. The U.S. Food and Drug Administration recognizes BII, BIA-ALCL, and BIA-SCC as potential risks and health complications of breast implantation. Black box warnings have now been added to breast implants. Framework: The IOWA model was chosen to guide the proposed evidence-based practice update and change. CONCLUSION: To identify those at risk for implant-related illnesses, universal screening for implantable devices is proposed. Universal screening for implantable devices is defined as assessing all patients at every healthcare encounter for the presence of an implantable device or a history of an implantable device. Universal screening for implantable devices determines which patients are at risk for implantable device-related systemic illness or cancer and enables illness-specific care promptly. Successful evaluation occurs when healthcare providers' standard practice screens all patients for implantable devices.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".