The Influence of Verification Jig on Framework Fit for Nonsegmented Fixed Implant‐Supported Complete Denture
Bibliographic record
Abstract
PURPOSE: The purpose of this retrospective study was to assess if there was a difference in the likelihood of achieving passive fit when an implant-supported full-arch prosthesis framework is fabricated with or without the aid of a verification jig. MATERIALS AND METHODS: This investigation was approved by the University of Rochester Research Subject Review Board (protocol #RSRB00038482). Thirty edentulous patients, 49 to 73 years old (mean 61 years old), rehabilitated with a nonsegmented fixed implant-supported complete denture were included in the study. During the restorative process, final impressions were made using the pickup impression technique and elastomeric impression materials. For 16 patients, a verification jig was made (group J), while for the remaining 14 patients, a verification jig was not used (group NJ) and the framework was fabricated directly on the master cast. During the framework try-in appointment, the fit was assessed by clinical (Sheffield test) and radiographic inspection and recorded as passive or nonpassive. RESULTS: When a verification jig was used (group J, n = 16), all frameworks exhibited clinically passive fit, while when a verification jig was not used (group NJ, n = 14), only two frameworks fit. This difference was statistically significant (p < .001). CONCLUSIONS: Within the limitations of this retrospective study, the fabrication of a verification jig ensured clinically passive fit of metal frameworks in nonsegmented fixed implant-supported complete denture.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".