Influence of immediate loading on healing of implants installed with different insertion torques – an experimental study in dogs
Bibliographic record
Abstract
OBJECTIVE: To evaluate the influence of different insertion torques on healing of implants loaded immediately or left unloaded. MATERIAL AND METHODS: In six Labrador dogs, all mandibular premolars and molars were extracted. After 4 months of healing, flaps were elevated, and two implant sites were prepared at each side of the mandible. The distal sites were prepared conventionally while the mesial sites were underprepared by 0.3 mm. As a consequence, different final insertion torques of about 30 Ncm at the distal and >70 Ncm at the mesial sites were recorded. Healing abutments were applied to the left and transmucosal abutments to the right side. Flaps were sutured, crown preparation of the upper right second and third premolars was performed, and impressions were taken. Within 24 h, crowns were cemented both to implants and teeth in the right side of the mouth. After 4 months, the animals were sacrificed and ground sections obtained for histological evaluation. RESULTS: A higher buccal bony crestal resorption and a more apical position of the coronal level of osseointegration were found at the loaded compared with the unloaded sites. MBIC% and percentages of peri-implant mineralized tissue (MB%) were higher at the loaded compared with the unloaded sites. Moreover, a higher MBIC% was found at the lower compared with the higher final insertion torque. CONCLUSIONS: Immediate loading does not seem to have a negative effect on osseointegration. High torque values for the immediate loading procedures were not necessary. Probably, low torque values, were sufficient to obtain primary stability and hence may provide better osseointegration than high torque value.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".