Implant treatment after traumatic tooth loss: A systematic review
Bibliographic record
Abstract
BACKGROUND/AIMS: Treatment after traumatic tooth loss is challenging and is currently guided by expert opinion and the individual patient situation. The aim of this study was to provide an overview on the outcome of dental implant treatment in the anterior maxilla after traumatic tooth loss, based on a systematic review of the existing evidence. MATERIALS AND METHODS: A systematic search of the literature was performed on PubMed, Cochran Library and Web of Science following the PRISMA guidelines based on a structured research question (PICO). All clinical studies of five patients or more with follow-up of at least 1 year after implant loading were included. Patients were at least 18 years of age. Cohen's Kappa-coefficient was calculated. The Newcastle-Ottawa Scale was applied to assess the quality of the included studies. Descriptive statistical methods were applied. RESULTS: Nine hundred and ninety-nine articles were identified through the systematic search. Finally, six articles were eligible for inclusion. The studies comprised prospective and retrospective cohort studies and case series. From these, 96 patients with 120 implants were included. The age ranged from 18 to 59 years. The survival rates of implants and superstructures were 97% and 95%, respectively, after a mean follow-up of 3.5 years. Mean marginal bone resorption was 0.56 mm (range 0.21-1.30 mm). Complication rates were 7% and 11% on implant and superstructure level, respectively. Patient-reported outcome measures and objective evaluations showed a high level of satisfaction with the aesthetic outcome. Bone augmentation was performed in 60 implant sites. Three patients underwent pre-surgical orthodontic treatment. The maxillary central incisor was the most frequently replaced tooth (70%). CONCLUSIONS: This systematic review revealed a low level of evidence on the outcome of dental implant treatment after traumatic tooth loss. Systematic reporting of treatment outcomes of tooth replacements after dental trauma is highly encouraged to further guide dentists for the benefit of these challenging patients.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".