Relative contributions of implant hydrophilicity and nanotopography to implant anchorage in bone at Early Time Points
Bibliographic record
Abstract
Abstract Objective To compare the contributions of implant hydrophilicity and nanotopography on anchorage in bone. The effect of elevated calcium surface chemistry on bone anchorage was also investigated. Materials and Methods A full factorial study design was implemented to evaluate the effects of ultraviolet (UV) light and/or sodium lactate (SL) and discrete crystalline deposition of nanocrystals (DCD) treatments on the osseointegration of dual acid‐etched (AE) titanium alloy (Ti6Al4V) and grit blasted and AE (BAE) commercially pure titanium (CpTi) implants. Sodium hydroxide (NaOH)‐treated CpTi implants were immersed in simulated body fluid (SBF) to increase calcium surface chemistry. Implants were placed in the femora of Wistar rats and tested using pull‐out testing (BAE implants: 5, 9, 14 days) or tensile testing (AE implants: 9 days, NaOH implants: 28 days). Results Ti6Al4V‐AE implants with DCD‐ and UV‐treated surfaces significantly increased bone anchorage compared with untreated Ti6Al4V‐AE alloy implants. Pull‐out testing of BAE‐CpTi implants with the DCD treatment showed increased disruption force values compared with surfaces without the DCD treatment at 5, 9 and 14 days by 4.1N, 13.9N and 15.5N, respectively, and UV‐treated implants showed an increase at 14 days by 8.4N. No difference was found between NaOH + SBF and NaOH + H2O groups. Conclusions Bone anchorage of implants was found to be improved by UV‐treating implants or nanotopographically complex surfaces. However, implant nanotopography was found to have a greater contribution to the overall bone anchorage and is more consistent compared with the time‐dependent nature of the UV 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 | 0.000 | 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 | 0.000 |
| 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".