A 5‐year cohort study on early implant placement with guided bone regeneration or alveolar ridge preservation with connective tissue graft
Bibliographic record
Abstract
PURPOSE: To assess on the one hand the 5-year outcome of early implant placement with guided bone regeneration (EIP/GBR) and on the other hand of alveolar ridge preservation with late implant placement and connective tissue graft (ARP/CTG). MATERIALS AND METHODS: Patients who had been treated with a single implant in the anterior maxilla in 2014 were selected for reevaluation in 2019. In the EIP/GBR cohort, implants were installed 4 to 8 weeks following extraction and the buccal contour was overbuilt by means of GBR. In the ARP/CTG cohort, collagen-enriched deproteinized bovine bone mineral was applied at the time of extraction and implants were installed 5 months later. All ARP/CTG cases received a buccal CTG 3 months after implant installation to reestablish buccal convexity. Primary outcomes were the pink esthetic score (PES) (Belser et al 2009) and the mucosal scarring index (MSI) (Wessels et al 2019). Clinical and radiographical parameters were secondary outcomes. RESULTS: Eighteen patients (9 females; mean age 52) in the EIP/GBR cohort, and 20 patients (9 females; mean age 38) in the ARP/CTG cohort were evaluated. PES was 6.28 following EIP/GBR. A (nearly) perfect aesthetic outcome defined as PES ≥8 was found in 3/18 cases. Perfect root convexity / soft tissue color and texture seemed most difficult to achieve. PES was 7.80 following ARP/CTG. A (nearly) perfect aesthetic outcome was found in 11/20 cases. MSI was 2.94 following EIP/GBR with 14/18 cases showing scarring. MSI yielded 0.50 following ARP/CTG with 6/20 cases showing scarring. In both cohorts, all implants survived, and none had developed periimplantitis. However, implants in the ARP/CTG cohort demonstrated a 5.4 times higher risk for periimplant mucositis than implants in the EIP/GBR cohort at 5 years follow-up. CONCLUSION: EIP/GBR and ARP/CTG showed acceptable long-term outcomes. The aesthetic outcome was particularly favorable following ARP/CTG, yet this should be interpreted with caution due to selection bias and because soft tissue grafting was not performed in the EIP/GBR cohort. Randomized controlled trial (RCTs) comparing EIP with ARP are required to assess the need for soft tissue grafting and to evaluate clinical, aesthetic, volumetric, and patient-reported outcomes. Only on the basis of such studies clinical recommendations can be made.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".