<scp>COVID</scp>‐19 as a factor associated with early dental implant failures: A retrospective analysis
Bibliographic record
Abstract
OBJECTIVES: To analyze the effect of COVID-19 on early implant failures and identify potential risk factors for early implant failure, concerning patient- and implant-related factors. MATERIALS AND METHODS: This retrospective study is based on 1228 patients who received 4841 implants between March 11, 2020, and April 01, 2022, at Erciyes University Faculty of Dentistry. COVID-19, age and gender of patients, smoking, diabetes, irradiation, chemotherapy, osteoporosis, the implant system, location, and characteristics of implants were recorded. At the implant level, univariate and multivariate generalized estimating equation (GEE) logistic regression was used to examine the effect of explanatory variables on early implant failure. RESULTS: The early implant failure rate was 3.1% at the implant level and 10.4% at the patient level. Smokers showed a significantly higher incidence of early implant failures compared to nonsmokers. (odds ratio (OR; 95% CI): 2.140 (1.438-3.184); p < 0.001). Short implants (≤8 mm) had a higher risk of early implant failure than long implants (≥12 mm) (OR (95% CI): 2.089 (1.290-3.382); p = 0.003). CONCLUSIONS: COVID-19 had no significant effect on early implant failure. Smoking and short implants were associated with a higher risk for early implant failures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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 teacher head, 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".