Interaction of Force‐Fitting and Surface Roughness of Implants
Bibliographic record
Abstract
BACKGROUND: Increased surface roughness may increase installation torque and thus appear to increase the initial stability of an implant. However, it is not immediately clear if the increased torque is attributable to an increase in the effective diameter of the implant or to increased resistance of the bone because of the greater roughness. PURPOSE: Force-fitting stresses arise when an implant is placed into a predrilled hole of smaller-diameter in bone. The purpose of this report is to discuss the interaction of force-fitting stresses and surface roughness effects and to develop some general guidelines as to clinical procedures based on this theory. MATERIALS AND METHODS: Solutions for the force-fitting stresses are derived from well-known equations of elasticity. RESULTS: Substantial force-fitting stresses on the order of several tens of MPa can be generated when a titanium cylinder is placed into a hole in bone, the diameter of which is only 100 microns smaller. CONCLUSION: When a hole slightly smaller than the implant diameter is prepared for implant placement, force-fitting stress increases installation torque and stability can be induced. Thus, large surface roughness of implants should not be viewed as an exclusive mechanism for providing a desirable level of initial fixity. Smaller roughness with the same mean diameter is equally effective.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".