Influence of Different Operatory Setups on Implant Survival Rate: A Retrospective Clinical Study
Bibliographic record
Abstract
BACKGROUND: Surgery performed under sterile operating conditions, as well as atraumatic surgery, has been stated to be among the most important requirements for successful osseointegration. However, there are few reports concerning the sterile surgical technique in association with implant placement, and the appropriate level of operatory setup is not fully known. PURPOSE: The purpose of this study was to analyze implant survival rate using a simplified surgical operatory setup compared with the use of the original Brånemark System (Nobel Biocare AB, Göteborg, Sweden) protocol. MATERIALS AND METHODS: A total of 1,285 consecutively treated patients were included in the study. Four thousand implants were placed during the period of 1985 to 2003. Group A (using the Brånemark System protocol) comprised of 654 patients and 2,414 implants. Group B (using a simplified operatory setup) comprised of 631 patients and 1,586 implants. Healing was evaluated after 6 months of clinical function. Failure was defined as the removal of implants because of nonosseointegration. Statistic analysis was performed using t-test for paired data. The level of significance was set at 5% for comparison of data. RESULTS: No significant difference with regard to complications and implant survival rate was found in the study. CONCLUSION: The result from the present study suggests that a simplified operatory setup does not affect the survival rate of oral implant treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".