Implant angulation: a measurement technique, implant overdenture maintenance, and the influence of surgical experience.
Bibliographic record
Abstract
PURPOSE: The purposes of this study were to develop a technique to measure the angulation between two implants and between each implant and reference planes, to analyze the relationship between the maintenance (adjustments and repairs) of ball-attachment mandibular implant overdentures and implant angulation, and to see if there is any correlation between surgeon experience and implant orientation. MATERIALS AND METHODS: Final casts of 41 patients who had received two-implant ball-attachment mandibular overdentures were used to measure implant angulations using digital photographs and plane geometry. The measured angles were compared with the number of adjustments and repairs of the prostheses and analyzed by surgeon experience for any trends. RESULTS: No significant relationships were found between number of adjustments and repairs and the interimplant angles. However, there was a significantly higher number of repairs when the lingual inclination of an implant was > or = 6.0 degrees (P = .033) or if the facial inclination was < 6.5 degrees (P = .036). Less experienced surgeons had a significantly greater tendency to place implants that diverged from each other in the frontal plane (P = .045) and with a facial or lingual inclination in the sagittal plane (P = .035). CONCLUSION: While interimplant angulation did not appear to affect prosthesis maintenance, individual implants with a lingual inclination > or = 6 degrees and a facial inclination < 6.5 degrees were associated with significantly more prosthesis repairs. There was a tendency for implants placed by less experienced surgeons to demonstrate greater inclination.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".