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Enregistrement W2413646611 · doi:10.1615/jlongtermeffmedimplants.v20.i1.30

Preface to Special Section: Retrieval Analysis of Implanted Medical Devices

2010· article· en· W2413646611 sur OpenAlexaboutno aff
William M. Mihalko

Notice bibliographique

RevueJournal of Long-Term Effects of Medical Implants · 2010
Typearticle
Langueen
DomaineMedicine
ThématiqueOrthopaedic implants and arthroplasty
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFood and drug administrationHealth technologyAgency (philosophy)Health careMedicineMedical emergencyPolitical scienceSociology

Résumé

récupéré en direct d'OpenAlex

Each year millions of patients improve their quality of life through surgical procedures that involve implanted medical devices. As the rising cost of health care continues to be debated within our country, it is clear that we must be certain that Americans are receiving the best, most cost effective health care treatments. Advances in medical technology continue to be a large part of the rising cost of health care, and the decision to provide the increased cost of care due to new technology must be determined from evidence-based studies. The Food and Drug Administration this past year issued an internal report where they stated that science-based reviews must be strived for and that the agency must first provide safe devices for patients while making sure not to impede the advancement and innovation of technology in the medical field. With the focus on both cost and innovation, it is clear that everything must be done to make certain that the devices we utilize in medicine are safe and that they are given a pathway to be improved on using science and lessons learned from device retrieval studies. Retrieval analysis studies of medical devices have been utilized for decades to learn lessons from failed devices removed at the time of revision or repeated procedures, as well as from those that functioned well for the life of a patient and were obtained at necropsy. The value of implant retrieval analysis in orthopaedic surgery has been well recognized in advancing implant longevity. If we use the example of total hip and knee arthroplasty, we know that analysis of devices has provided insight into wear, corrosion, design characteristics, and material issues that have advanced the design and longevity of these devices. Prosthetic implants retrieved at revision surgery (for implant failure) or devices retrieved postmortem from patients with clinically successful reconstructions provide a unique set of specimens that can be studied to evaluate the effect of the implant on the host environment and vice versa. In this issue, there are two articles that try to determine commonalities of patellar polyethylene buttons from total knee arthroplasty, with one obtained from necropsy retrievals and the other from implants obtained at time of revision surgery. A systematic analysis of retrieved components, in combination with histologic, radiographic, and clinical data, can provide valuable insights into the mechanisms of failure of the biomaterials and implant designs used in joint replacement applications. If computer modeling can be added to properly predict wear patterns and kinematics of the joint, then the first steps toward intraoperative computer assistance for optimal functionality and implant longevity may be realized. This type of analysis can be seen in this issue’s article that utilizes a 3D dynamic TKA model to show how the kinematics and contact stresses change after wear of the polyethylene insert (Williams et al.). The first retrieval observations were performed for failures of implants during their subsequent revision surgeries. While these studies gave valuable insight into the failure modes of early designs and biomaterials, it was not until the 1980s when donor-related studies were organized. At that time, usually the establishment of a retrieval program within a practice of orthopaedic surgeons allowed for the analysis of a series of patients who had a specific implant utilized at the time of surgery. There are few retrieval programs that encompass a large variety of implants, and many times those laboratories only receive the implant itself without the surrounding joint and musculoskeletal tissues. It has been evident from past studies that several significantly important modes of wear and or implant failure have been reported that have allowed changes in the field of hip and knee arthroplasty to further the design-related and surgicalrelated techniques in the field. We must continue to update our investigative techniques of these devices, and this issue also has reports from London, Ontario, investigators evaluating new techniques to evaluate the wear of polyethylene components. Ultimately, it is the patient and the improvement in their quality of life that benefit from retrieval analysis studies. It is imperative that the field of medicine continue these kinds of studies to investigate newer, more modern implant designs and surgical techniques since the in vitro testing and the computer modeling techniques cannot always predict the in vivo wear or failure mechanisms that may have a negative impact on the longevity or functional result for the patient.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,139
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,320
Écart entre enseignants0,310 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2010
Routes d'admission1
Résumé présentoui

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