Implant framework misfit: A systematic review on assessment methods and clinical complications
Bibliographic record
Abstract
BACKGROUND: The fit of implant-supported prostheses is of prime importance for the long-term success of implant therapy. PURPOSE: This systematic review aimed to evaluate recent evidence on current techniques for assessing implant-framework misfit, its associated strain/stress, and whether these misfits are related to mechanical, biological, and clinical consequences. MATERIALS AND METHODS: An electronic search for publications from January 2010 to October 2020 was performed using the Pubmed, Embase, Web of Science, and Cochrane Library databases with combined keywords on implant-framework misfit assessments and related clinical complications. Inclusion and exclusion criteria were applied. After full-text analyses, data extraction was implemented on current techniques of misfit assessment and the relationship between the misfit and the induced strain/stress. RESULTS: A total of 3 in vivo and 92 in vitro studies were selected, including 47 studies on quantifying the degree of implant-framework misfit with dimensional techniques, 24 studies measuring misfit-induced strain/stress with modeling techniques, and 24 studies using both methods. The technical details, advantages, and limitations of each technique were illustrated. The correlation between the implant-framework misfit and the induced strain/stress has been revealed in vitro, while that with the biological complications and implant/prostheses failure was weak in clinical studies. CONCLUSIONS: Dimensional and modeling techniques are available to measure the implant-framework misfit. The passivity of implant-supported fixed prostheses appeared related to the induced strain/stress, but not the clinical complications. Further studies combining three-dimensional (3D) assessments using dimensional and modeling techniques was needed.
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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 | 0.020 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".