Re: COVID‐19 as a factor associated with early dental implant failures: A retrospective analysis—There is a need for research on the effect of COVID‐19 vaccination on dental implant failure: Implications for policymaking and insurance
Notice bibliographique
Résumé
We are writing to address (1) the research conducted by Sezer and Soylu1 titled “COVID-19 as a factor associated with early dental implant failures: A retrospective analysis,” and (2) explore the need of critical evaluation and future research on the effect of COVID-19 vaccination on dental implant failure since it has implications for policymaking and is of importance to the public. For the first part, while we acknowledge the significance of Sezer and Soylu1 contribution as the first study to analyze the effect of COVID-19 on early implant failure, we believe there are crucial aspects that warrant further exploration to ensure patient safety and improve dental implant outcomes. For the second part to discuss in this letter, considering the observed trends in North American private practices, which remains unpublished, of increased implant failure rates in patients who received the COVID-19 vaccine before implant surgery, it is necessary to conduct research to understand the underlying mechanisms and potential risk factors, which to date has not been reported. This research shall explore the incidence of peri-implantitis in vaccinated individuals and differentiate between early and late implant failures to better assess the temporal relationship with vaccination. Interestingly, the observed increase in implant failure, regardless of operator experience and implant brand, warrants focused attention from the dental community and policymakers, as it suggests potential concerns beyond individual practitioner performance. However, the impact of operator experience and implant brand on implant failure rates should be investigated. From a policymaking perspective, the potential association between COVID-19 vaccination and dental implant failure necessitates proactive consideration. If future research confirms a significant link, guidelines should be developed or modified to aid dental implant practitioners in assessing patient vaccination status as part of the pre-implant evaluation process. Insurance providers should also be prepared to adjust policies to accommodate potential revision surgeries or additional treatments resulting from increased implant failure rates. In conclusion, despite COVID-19 and its variants can impact oral health,5 the study by Sezer and Soylu serves as an essential starting point for exploring the lack of relationship between COVID-19 and dental implant failure. However, critical evaluation reveals several limitations, warranting cautious interpretation of the findings. Also, we wish the relationship between a COVID-19 vaccinated population and dental implant failure is examined in the future as nothing available in the literature examining despite the observations from our colleagues in private practice, and other oral adverse reactions observed after COVID-19 immunization.6 Thus, to ensure patient safety and enhance the quality of implant dentistry, we urge Clinical Implant Dentistry and Related Research to support future research that addresses the highlighted concerns and investigates the implications for policymaking and insurance in the field. Thank you for considering this perspective, and we look forward to witnessing the advancement of implant dentistry through evidence-based research in your prestigious journal. Kelvin I. Afrashtehfar: Conceptualization, literature review, drafting the article, critical revision and approval of manuscript. J. W. Martin Kim: Conceptualization, critical revision and approval of manuscript. The authors declare no conflicts of interest.
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 | 0,017 | 0,071 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».