Survival of Immediately Provisionalized Dental Implants: A Case‐Control Study with up to 5 Years Follow‐Up
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate the survival rate of immediately provisionalized implants with up to 5 years follow-up. MATERIALS AND METHODS: The study consisted of 226 patients, 113 consecutive patients with immediately provisionalized dental implants (cases) and 113 randomly selected, age-, gender-, and implant position-matched controls with conventional late implant loading. Survival rate and incidence of complications were recorded. RESULTS: Follow-up ranged from 6 to 60 months. Smoking was reported by 20.8% of patients. Maxillary incisors and mandibular lateral incisors were the most common areas for implant placement. Conventionally loaded implants were narrower (p = .03) and shorter (p = .001). Immediate implantation into a fresh extraction socket was performed in 69% of the cases and in 36.3% of the controls (p = .001). Implant survival rate was 96.5%. Of the eight failed implants, six were immediately provisionalized and two were conventionally loaded. No statistically significant difference was found in survival rates between groups (p > 0.05). Five of the failed implants (case group) were immediately loaded implants placed in fresh extraction sockets. CONCLUSION: Immediate implant provisionalization achieved similar high success rates compared with the conventional, delayed approach. As immediate implant provisionalization is mainly desired in the anterior region, the high success rates are encouraging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".