Reply: Not All Breast Implants Are Equal: A 13-Year Review of Implant Longevity and Reasons for Explantation
Bibliographic record
Abstract
Sir: We are grateful for the opportunity to respond to Charles Randquist’s letter regarding our article, “Not All Breast Implants Are Equal: A 13-Year Review of Implant Longevity and Reasons for Explantation.”1 We read with interest Dr. Randquist’s experience with Biocell implants (Allergan, Inc., Irvine, Calif.) and the improvements in his outcomes after applying the five Ps to his breast implant practice.2 Although we agree that his results are more than satisfactory after applying the five Ps to placing Biocell implants, we have some reservations with his logic. The ideas of poor patient selection, surgical technique, and specific postoperative regimens to optimize tissue integration within Biocell implants have previously been suggested as correctable measures to prevent the incidence of double capsules and late seromas seen with Biocell implants.3 Despite these recommendations, we are still explanting Biocell implants with concerning clinical and intraoperative findings. We agree that, in select patients, Biocell implants may be able to produce impressive results. However, the results from our study suggest that there remains a large population of patients who are receiving Biocell implants who are not only achieving suboptimal aesthetic outcomes, but are also presenting with inflammatory findings. The high rate of implant performance failure observed in our study with Biocell implants suggests that many plastic surgeons are struggling with this phenomenon, not just one single surgeon,1 and the low rates of follow-up seen from the core studies further suggest that the majority of plastic surgeons placing these implants may not be seeing these negative outcomes.4,5 We believe that the design of our study may preferentially capture more of these patients, as patients with poor results may choose to see a different surgeon for their explantation rather than the originally treating surgeon. This is a consecutive series of explantations in a single surgeon’s practice representing implants that were placed by a variety of surgeons. It presents an unbiased look at how an array of commonly used breast implants performed from the consumer’s perspective, and shows the reader when and why they were removed. For many years, the insinuation has been made that complications related to use of textured implants occur because of inadequate skill of surgeons using these devices. The reality is that average surgeons place the majority of devices, and if certain devices only work under exceptional circumstances in the hands of exceptional surgeons, consumers deserve that knowledge. The other reality is that industry-supported recommendations for device use change with time and will continue to do so, resulting in most devices being inserted without the advantage of the most current knowledge. Textured devices have received a lot of negative attention because of their association with anaplastic large cell lymphoma. The important message contained in this work is that these devices are associated with an array of problems that we characterize as device failure that, although less dangerous than anaplastic large cell lymphoma, are far more common. DISCLOSURE The authors do not have any financial disclosures. Aaron C. Van Slyke, M.D., M.Sc.Nicholas J. Carr, M.D.Division of Plastic SurgeryDepartment of SurgeryUniversity of British ColumbiaVancouver, British Columbia, Canada
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".