Evaluation of the effect of self‐cutting and nonself‐cutting thread designed implant with different thread depth on variable insertion torques: An histomorphometric analysis in rabbits
Bibliographic record
Abstract
PURPOSE: To evaluate of the effect of self-cutting and nonself-cutting thread designed implant with different thread depth on variable insertion torques. To evaluate the bone volume (BV) and bone to implant contact (BIC) in these variables MATERIALS AND METHODS: Mainly two thread design, V-shaped thread which is self-cutting and power thread design, which is nonself-cutting implants were considered for this study with a variation in thread depth of 0.4 and 0.6 mm for both the designs, respectively. A total of 32 CAD designed machined surface implant prototypes were manufactured of 4 mm in diameter and 8 mm in length were made, which were machined surfaced, which was placed in the femur of 16 New Zealand white rabbits. These were categorized under 2 groups; Group 1 and Group 2 with insertion torques of <30 and >50 Ncm, respectively. After 4 weeks of healing, rabbits were sacrificed and histomophometric and histologic analyses were done to evaluate the bone response. RESULTS: Significantly, more BIC was recorded for high torque implants compared with low torque in power-shaped thread design (P value = .01*). BV for new bone formation was statistically significant for V-shaped thread design in high torque when compared with low torque (P value = .02*). CONCLUSION: The effect of the depth of the thread design was significant for the power-shaped design in enhancing BIC when compared with V-shaped thread design. With high torque V-shaped thread design had more new bone formation as compared with power-shaped thread design.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".