Primary Stability Determination by Means of Insertion Torque and RFA in a Sample of 4,135 Implants
Bibliographic record
Abstract
OBJECTIVES: The aims of the present study are to evaluate the primary stability of a sample of 4,135 implants and to investigate the correlations between primary stability and mechanical characteristic of the implant and bone density at insertion time. MATERIAL AND METHODS: The study was conducted from March 2002 to January 2009 at a private practice in Bologna (Italy). Patients were eligible for the study if they needed the insertion of single or multiple implants. Bone density, length, and diameter of each implant were recorded. During surgery for each implant, peak insertion torque (IT) was recorded; the resonance frequency analysis (RFA) values were also collected. Finally, it was recorded whether an implant was lost or removed at an early stage (within 6months from insertion surgery). RESULTS: A total of 1,045 consecutive patients were included in the study. A total of 4,135 of implants were inserted. The sample presented 1,184 implants inserted in a postextractive site. The mean peak IT was 34.82±19.36. The mean RFA was 71.57±10.63 implant stability quotient. Spearman correlation analysis shows the presence of a weak positive correlation between IT and RFA. The statistical analysis shows a relevant dependency between IT and bone quality and a very weak dependency between RFA and bone quality. Again, the statistical analysis shows a quite weak correlation between length or diameter and IT, but it shows a relevant correlation between length and RFA. Postextractive implants presented a higher mean IT and a lower RFA compared with implants inserted in healed sites. Twenty-eight (0.7%) implants were considered to have failed and removed within 6months. CONCLUSIONS: The results show that the implants studied obtain a good primary stability with a standard protocol. The IT and RFA appear as two independent features of primary stability. Data show that only IT is influenced by bone density as well as only RFA is correlated to the length of implants used. Finally, it is possible to obtain a good primary stability also in postextractive sites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".