Reply: Not All Breast Implants Are Equal: A 13-Year Review of Implant Longevity and Reasons for Explantation
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,005 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,012 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».