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Record W2622590156 · doi:10.1097/prs.0000000000003615

Late Seromas in Natrelle 410 Form-Stable Silicone Breast Implants

2017· letter· en· W2622590156 on OpenAlexaffabout
Bryan Chung, Elizabeth J. Hall‐Findlay

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2017
Typeletter
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsBanff Centre
Fundersnot available
KeywordsSeromaImplantMedicineBreast implantSurgeryGeneral surgery

Abstract

fetched live from OpenAlex

Sir: There are problems with the conclusions in the article by McGuire, Reisman, and Murphy in the January of 2017 issue of Plastic and Reconstructive Surgery.1 We cannot help but be skeptical when we see an article that was written by industry (Allergan) and where the senior author is paid large consulting fees by the same company (https://openpaymentsdata.cms.gov/physician/1014351).2 Their conclusions are biased and misleading. The authors discuss the rate of late seroma and they state that “…as the rate observed in this study is similar to that reported for breast implants in general, it is unlikely that the Natrelle 410 implant itself confers an incremental risk of late seroma relative to other implants.” The authors give no reference for this claim and ignored Spear’s article that concluded “Biocell textured implants were more likely to be associated with late seromas than were smooth shell implants.”3 Textured implants have been involved in 55 of 60 cases in the review by Park et al.,4 six of six cases in the series reported by Pinchuk and Tymofii,5 eight of eight cases in the series by Mazzocchi et al.,6 and 27 of 28 cases in the series by Spear et al.3 Although the causal relationship between textured implants and late seroma has not yet been elucidated and despite the limitations of these studies (discussed below), this association is very strong. It does not seem appropriate to implicate that the Natrelle 410 breast implant, which is textured, is not as “incrementally risky” as nontextured implants. I (E.J.H.F.) have been in practice for 34 years, and colleagues of my generation rarely if ever saw a late seroma with a smooth implant. The problem is now highlighted in international conferences where whole panels are being dedicated to the discussion of late seromas (e.g., the Latin American Society of Plastic Surgeons, in Panama City, Panama, June 1 through 4, 2010), and is a recent phenomenon that began when textured implants were introduced and almost universally adopted. All case series to date on late seromas have been subject to lead-time bias with nonuniform exposure times between subjects that were not adjusted for in analysis (i.e., unadjusted estimates of late seroma in subjects with shorter follow-up times causes underestimation of the true rate of late seroma). In this article, 16 of 26 late seromas were reported under the mean follow-up time, with a skew toward seromas under 3 years. The true rate of late seromas is not well-estimated given this limitation and uncontrolled exposure time. The association between implant age (it was not clear whether implant age was the same as time after implantation) and late seroma supports this inherent bias and underestimated rate. Lastly, the authors did not include any time-based outcomes, but reported a Cox proportional hazards regression model. This makes us wonder what multivariate models were being used to calculate adjusted risk ratios. The model from the “backward elimination technique” was not presented, and postregression analysis, including discussion of possible interaction and confounding, was not reported. We feel that the risk of late seroma is likely underestimated in the literature. Controlling for patient exposure time should be required in postmarketing surveillance database analysis to avoid issues such as lead-time bias. DISCLOSURE The authors have no financial interest to declare in relation to the content of this communication. Bryan Chung, M.D., Ph.D.Elizabeth J. Hall-Findlay, M.D.Banff Plastic SurgeryBanff, Alberta, Canada

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.238
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes2
Has abstractyes

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