Three‐Dimensional Topographic and Metrologic Evaluation of Dental Implants by Confocal Laser Scanning Microscopy
Bibliographic record
Abstract
BACKGROUND: Surface topography of dental implants has changed during the past few years; however, the last systematic study on this topic is dated 1993. PURPOSE: The aim of this study was to correlate dental implants by surface analysis. MATERIALS AND METHODS: A microtopographic analysis of 35 dental implants was performed using confocal laser scanning microscopy. Roughness value (Sa) and developed surface area (ratio, Sdr) were calculated. Implants were grouped according to their surface treatment: "minimally rough" with no further surface treatment (n = 2); ablative structured using etching or blasting (n = 17); titanium plasma spray coated (TPS; n = 9); coated with hydroxyapatite (HA; n = 7). RESULTS: Most implants (n =16) showed Sa values between 3.0 and 5.0 microm. The developed surface area has a mean value of 13.5 and an SD of 6.52. Minimally rough implant surfaces show the lowest Sa values (mean 0.5 microm). Implants with ablative surface treatment have mean Sa values of 3.1 microm. Both groups with additive surface treatment (TPS and HA) present similar roughness values with a mean of 6.0 microm and 5.8 microm, respectively. Ratio Sdr ranges from 3.1 for the minimally rough implants to 11.4 for the ablative treated implants and 14.3 for TPS-coated and 18.4 for HA-coated implants. There is a significant difference between the roughness and ratio values of the different groups. The topographic images show a typical surface according to the underlying surface treatment. CONCLUSIONS: We can confirm the "classic" grouping of dental implants by type of surface treatment into the groups minimally rough, ablative, TPS coated, and HA coated as these treatments lead to different ascending Sas; however, the additional value of the ratio Sdr including both spatial and amplitude aspects of the surface could not be confirmed in this study. Functional parameters describing the topographic differences are still lacking.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".