Effects of sterilization and disinfection methods on digitally designed surgical implant guide accuracy: An in vitro study
Bibliographic record
Abstract
INTRODUCTION: Surgical guides are commonly used to assist with dental implant placement. This study investigated the effects of five sterilization and disinfection methods on the accuracy of implant guides. METHODS: Thirty surgical guides (five in each group) were designed and printed (with digital light processing technology) using different sterilization or disinfection methods categorized into six groups: hydrogen peroxide sterilization (group one); glutaraldehyde sterilization (group two); autoclaving (group three); plasma sterilization (group four); iodophor disinfection (group five); and blank group (group six). Verification was determined using three methods: distance and angle between the cross-shaped marks, deformation after superimposing the guides, and displacement and axial changes in the virtual implant. RESULTS: , glutaraldehyde, autoclaving, and iodophor groups regarding deformation after superimposing the guides and the distance and angle between the cross-shaped marks. The average labial deformation values (mm) of the first through fifth groups of guides were 0.283, 0.172, 0.289, 0.153, and 0.188, respectively. All groups were statistically different from the blank group for displacement and axial changes of the virtual implant (p < 0.05). CONCLUSION: The sizes of almost all surgical guides changed after sterilization and disinfection treatments, with between-group differences. Plasma sterilization was more suitable for surgical guide sterilization because of the smaller deformations after treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".