Transitional implants in computer‐assisted implant surgery and fixed complete‐arch provisionalization: A retrospective case series
Bibliographic record
Abstract
INTRODUCTION: Using mini implants as transitional implants (TIs) for complete arch implant-supported rehabilitations may overcome limitations associated with mucosa-supported surgical guides and facilitate immediate fixed provisionalization. This study aimed to assess the success of TIs in supporting surgical guides for implant placement and fixed provisional prostheses. METHODS: Patients who received TIs between 2012 and 2023 for a complete arch implant-supported prosthesis were evaluated retrospectively. Patient demographic data, TI functionality in supporting a surgical guide and supporting a complete arch provisional prosthesis, and dates of TI placement and regular implant placement were collected. Descriptive statistics were used to determine the survival rate and success rate for TIs. RESULTS: Twenty-six patients, 35 jaws, 136 TIs, and 216 regular implants were included. The survival rate of TIs was 74.26%; however, the use of TIs yielded success in 97% of jaws for supporting a surgical guide and a fixed complete-arch provisional prosthesis throughout the complete provisional phase. An average of 4 TIs per maxilla and 3 TIs per mandible supported surgical guides. Thirty-five provisional prostheses were placed on an average of 4 TIs in the maxilla and 3 TIs in the mandible. Thirty-four provisional prostheses were successfully supported by TIs and regular implants until final restoration delivery. The survival of regular implants placed in conjunction with the use of TIs was 98%. CONCLUSIONS: Using TIs to support a surgical guide and provisional prosthesis may be a predictable approach with a high success rate. All surgical guides planned to be supported on TIs were successful. Despite premature loss or replacement of TIs, this approach was able to support most provisional prostheses until the regular implants could be loaded.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".