Preface to Special Section: Retrieval Analysis of Implanted Medical Devices
Notice bibliographique
Résumé
Retrieval Analysis of Implanted Medical DevicesEach 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 eff ective 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.Th e 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 fi rst provide safe devices for patients while making sure not to impede the advancement and innovation of technology in the medical fi eld.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.Th e 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 eff ect 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 fi rst steps toward intraoperative computer assistance for optimal functionality and implant longevity may be realized.Th is 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.).Th e fi rst 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 specifi c implant utilized at the time of surgery.Th ere 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
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 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,043 | 0,034 |
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 ».