Cleaning, Disinfection, and Sterilization Protocols Employed for Customized Implant Abutments: An International Survey of 100 Universities Worldwide
Bibliographic record
Abstract
PURPOSE American and European standards recommend sterilization of customized abutments before connecting them to implants, as customized abutments are considered semi-critical medical devices. Since standardized procedures could not be identified in the literature on implantology, this survey evaluated the protocols employed at different universities worldwide to clean, disinfect, and/or sterilize customized abutments before their connection to bone-level implants. MATERIALS AND METHODS The survey took place between October 2015 and January 2016. A single question acquiring information on how customized abutments were treated prior to connection to the implants was sent by email to researchers affiliated at 100 universities worldwide. To avoid any bias, the survey was kept rigorously anonymous. A total of 100 universities from Europe (56), USA and Canada (25), Latin America (9), South Africa (1), Asia (6), and Australia and New Zealand (3) were invited to participate in the survey. RESULTS Altogether, 85 universities responded to the survey question, and 22 (25.9%) declared that no cleaning protocols were adopted. More than half of the respondents (n = 49, 57.6%) performed only one of the three procedures required by the standards (cleaning, disinfection, or sterilization). Twelve respondents (14.1%) adopted two procedures, and only two universities performed all three required procedures (2.4%). CONCLUSION This survey indicated substantial heterogeneity in treating customized abutments before connecting them to implants. This study demonstrated that the majority of the universities applied either cleaning, disinfection, or sterilization which may not meet the prevailing standards.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".