Long‐Term Follow‐Up of 2.5‐mm Narrow‐Diameter Implants Supporting a Fixed Prostheses
Bibliographic record
Abstract
BACKGROUND: The use of narrow-diameter implants (NDIs; <3.75 mm) constitutes an alternative to bone augmentation procedure. Long-term evaluation of NDIs with a diameter <3.0 mm is still lacking. PURPOSE: Analyze the long-term outcomes of 2.5-mm NDIs splinted to regular-sized implants for supporting partial and complete fixed prostheses. MATERIALS AND METHODS: Patients charts were retrospectively analyzed to select patients treated by the insertion of at least one 2.5-mm two-piece implant before July 2005. The study was based on the available charts (no patient was recalled). Patient's demographic data were described. The known implant length was used as a reference to calibrate the linear measurements of marginal bone loss on digital periapical radiograph. Implant details, survival and prosthetic complications were analyzed. RESULTS: Thirty-seven 2.5-mm implants placed in 20 patients (mean age at surgery: 54.05 ± 9.7 years) in maxilla and mandible were included and evaluated. The implants' mean follow-up time since insertion was 6.5 ± 3.2 years (range 0 to 9.7 years). The follow-up time was more than 7 years for 22 implants. One implant failed due to lack of osseointegration. Two prosthetic complications (connector and porcelain fracture) occurred. The survival rate was 97.3% for implants and 92.0% for prostheses. The mean marginal bone loss at the mesial and distal aspect was 0.70 ± 0.55 and 0.72 ± 0.56 mm, respectively. CONCLUSIONS: When dental implants of 2.5 mm in diameter are splinted by a fixed prosthesis, long-term favorable outcomes could be obtained.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".