Clinical Experiences with Laser‐Welded Titanium Frameworks Supported by Implants in the Edentulous Mandible: A 10‐Year Follow‐Up Study
Bibliographic record
Abstract
BACKGROUND: Long-term follow-up studies for more than 5 years are not available on laser-welded titanium frameworks. PURPOSE: To report and compare 10-year data on implant-supported prostheses in the edentulous mandible provided with laser-welded titanium frameworks and conventional gold alloy frameworks. MATERIALS AND METHODS: Altogether, 155 patients were consecutively treated with prostheses at abutment level with two generations of fixed laser-welded titanium frameworks (test groups). A control group of 53 randomly selected patients with conventional gold alloy castings was used for comparison. Clinical and radiographic 10-year data were collected for the three groups. RESULTS: All patients followed-up for 10 years (n=112) still had fixed prostheses in the mandible (cumulative success rate [CSR] 100%). The overall 10-year cumulative success rate (CSR) was 92.8 and 100.0% for titanium and gold alloy frameworks, respectively. Ten-year implant cumulative survival rate (CSR) was 99.4 and 99.6% for the test and control groups, respectively. Average 10-year bone loss was 0.56 (SD 0.45) mm for the titanium group and 0.77 (SD 0.36) mm for the control group (p < 0.05). The most common complications for titanium frameworks were resin or veneer fractures, soft tissue inflammation, and fractures (12.9%) of the metal frame. Loose and fractured implant screw components were below 3%. CONCLUSION: Excellent overall long-term results with 100% CSR could be achieved with the present treatment modality. Fractures of the metal frames and remade prostheses were more common for the laser-welded titanium frameworks, and the first generation of titanium frameworks worked poorly when compared with gold alloy frameworks during 10 years (p < 0.05). However, on average more bone loss was observed for implants supporting gold alloy frameworks during 10 years. The reasons for this difference are not clear.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".