Survival Rates and Bone Level Changes around Porous Oxide‐Coated Implants (<scp>T</scp>i<scp>U</scp>nite™)
Bibliographic record
Abstract
PURPOSE: This prospective study evaluated implant survival rates and crestal bone changes for porous oxide-coated (TiUnite, Nobel Biocare AB, Gothenburg, Sweden), parallel-walled implants. MATERIALS AND METHODS: All patients receiving TiUnite (porous oxide-surfaced implants [POS]) implants were entered into a database (Triton Tracking System) starting February 1999. Survival rates were calculated from the date of implant placement and related to surgical method of placement (two-stage buried, flapless, immediate placement, immediate placement flapless, one stage), bone quality, and implant characteristics. Failed and nonfailed implants were compared with respect to changes in mean proximal bone levels and the presence of radiolucent areas around the implant apex (shadows). RESULTS: Four hundred nine patients received 817 porous oxide-coated implants, of which 38 failed. Using the last office visit as the censoring date, the cumulative survival date was 93%. The failure rate was independent of bone quality or quantity; implant diameter or length; and surgical method. For the 102 surviving implants, there was no significant change in the average crestal bone loss (+0.13 mm with a standard error, 0.17). For the 17 failing implants, the average crestal bone loss was -4.14 mm (standard error, 0.55). This difference between bone levels of failing and nonfailing implants was highly significant (p < .0001). There was no difference in the prevalence of radiographic shadows around failing and nonfailing implants at time of placement (p < .16). CONCLUSION: Results from this prospective clinical study indicate that 7% of TiUnite surfaced implants failed for unknown reasons. Failing implants were characterized by significant bone loss but not by the presence of shadows.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".