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
Bibliographic record
Abstract
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part to discuss in this letter, considering the observed trends in North American private practices, which remains unpublished, of increased implant failure rates in patients who received the COVID-19 vaccine before implant surgery, it is necessary to conduct research to understand the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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".