Dimensional Tolerances and Assembly Accuracy of Dental Implants and Machined Versus Cast‐On Abutments
Bibliographic record
Abstract
BACKGROUND: The clinical application of prosthetic components obtained by different manufacturing processes lacks technological foundation: the dimensional tolerance of individual parts and their assembly accuracy are not known. The rotational misfit (RM) of the hexagonal connection is critical in single-tooth implant restorations, but no standard control procedures are available for its evaluation. PURPOSE: The research aimed at proposing a new protocol for the dimensional assessment of implant-abutment connections, based on noncontact measurement and statistical data processing. The procedure was applied to machined- and cast-on abutments, as well of the matching implants. MATERIALS AND METHODS: Three groups of five abutments each were studied: machined titanium abutments, pre-machined calcinable abutments before casting procedures and the same specimens after casting. A group of five corresponding implants was considered as well. Twice the apothem was measured on each hexagon through an optical measuring microscope. The data were processed to obtain the international tolerance (IT) grade. The RM was then calculated using the apothems of the external and the internal hexagon. RESULTS: All the components were classified between IT8 and IT9, and the maximum RM was around 3-4° for all the assemblies, inferior to the critical limits for the screw joint stability. CONCLUSION: An original measuring protocol was developed, independent of parts assembly and based on ITs. An objective dimensional characterization of prosthetic components and assemblies has been achieved, which is the basis for their reliability in clinical applications.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".