Acid-etched microtexture for enhancement of bone growth into porous-coated implants
Bibliographic record
Abstract
We designed an in vivo study to determine if the superimposition of a microtexture on the surface of sintered titanium beads affected the extent of bone ingrowth. Cylindrical titanium intramedullary implants were coated with titanium beads to form a porous finish using commercial sintering techniques. A control group of implants was left in the as-sintered condition. The test group was etched in a boiling acidic solution to create an irregular surface over the entire porous coating. Six experimental dogs underwent simultaneous bilateral femoral intramedullary implantation of a control implant and an acid etched implant. At 12 weeks, the implants were harvested in situ and the femora processed for undecalcified, histological examination. Eight transverse serial sections for each implant were analysed by backscattered electron microscopy and the extent of bone ingrowth was quantified by computer-aided image analysis. The extent of bone ingrowth into the control implants was 15.8% while the extent of bone ingrowth into the etched implants was 25.3%, a difference of 60% that was statistically significant. These results are consistent with other research that documents the positive effect of microtextured surfaces on bone formation at an implant surface. The acid etching process developed for this study represents a simple method for enhancing the potential of commonly available porous coatings for biological fixation.
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 | 0.000 | 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".