Factors influencing marginal bone loss at a hydrophilic implant design placed with or without GBR procedures: A 5‐year retrospective study
Bibliographic record
Abstract
BACKGROUND: Ongoing marginal bone loss is a threat to the longevity of implant-supported prostheses. AIM: The aim of the present study was to retrospectively evaluate the survival rate and factors affecting marginal bone levels at a hydrophilic implant design after 5 years in function. MATERIALS AND METHODS: The study group consisted of 51 consecutive patients previously treated with 159 hydrophilic implants (Neoss Straight Proactive implants) and scheduled for annual check-ups with clinical and radiographic examinations during 5 years. Data were compiled for the entire study population as well as for two subgroups: one where guided bone regeneration (GBR) was performed (91 implants) and the other where no GBR procedures (68 implants) were performed. Marginal bone levels were measured from peri-apical radiographs taken at placement and annual follow-ups. Statistical analyses were applied to evaluate the effect of different factors on marginal bone remodeling. RESULTS: Two implant failures, one from each subgroup, occurred during the first year of function resulting in an overall cumulative survival rate (CSR) of 98.7% after 5 years of loading. The mean marginal bone loss amounted to 0.7 ± 0.7 mm after 1 year and 0.8 ± 0.6 mm after 5 years. No implants showed more than 3 mm bone loss after 5 years. Age, gender, implant position, biotype, implant diameter, implant length, indication, surgical/loading protocol, and ISQ at prosthesis delivery were found to affect bone remodeling. No significant differences or correlations were seen for smoking, jaw, bone quantity, bone quality, GBR, sinus lift, and ISQ at implant placement. CONCLUSIONS: The present implant design performed well with few failures and minimal marginal bone loss after 5 years of loading. Marginal bone remodeling at implants is a complex phenomenon, which is affected by many patient-, procedure-, and implant-related factors that need to be further investigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".