Turned, Machined Versus Double‐Etched Dental Implants In Vivo
Bibliographic record
Abstract
BACKGROUND: Positive effects on the clinical outcome of moderately rough implant surfaces are described. Intercomparison of clinical data, however, is rarely found. PURPOSE: The aim of this study was to compare the clinical results of two macroscopically identical implants, the one with a turned, machined and the other with an etched surface. MATERIALS AND METHODS: In a retrospective cohort study, the included implants followed the criteria: standard surgical protocol, >12 months in situ; minimally rough self-threading implants with a turned, machined surface (Mk II Nobel Biocare AB, Göteborg, Sweden], n=210); etched implants of the same macrodesign (3i Implant Innovations Inc., Palm Beach Gardens, FL, USA], n=151), length > or = 10 mm. Clinical data and implant success were rated. Resonance frequency analysis (RFA) and Periotest (Siemens AG, Bensheim, Germany) were measured and related to the corresponding implant survival rate in the respective group. RESULTS: The total number of implants was 361, of which 264 (73%) were subject to clinical reexamination. RFA and Periotest could be recorded in 25% of the implants. Neither clinically relevant nor statistically significant differences between the surface designs were found in the RFA (64 +/- 8.6 vs 63 +/- 9.7), in Periotest (-2 +/- 3.3 vs -1 +/- 5.1), and in mean survival periods (49 months, 95% confidence interval CI]: 46-51 months, for the turned vs 46 months, 95% CI: 43-49 months, for the double-etched implant). After osteoplastic procedures, a significantly higher rate of implant losses in the turned, machined implant group was observed (17 vs 1) with a mean survival period of 43 (40-46) months for the turned and 46 (45-48) months for the double-etched implants. CONCLUSION: No difference between implants with two different minimally rough surfaces was found. A positive effect of surface roughness is observed in poor quality bone, but the pivotal proof of this effect is 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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".