Stability Measurements of Osseointegrated Implants Using Osstell in Partially Edentulous Jaws after 1 Year of Loading: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: The introduction of resonance frequency analysis (RFA) as a commercially available technique has made it possible to measure implant stability in implant stability quotient (ISQ) units at any time during the course of implant treatment and loading. However, no information on normal ISQ levels can be found in the literature. PURPOSE: The aim of this pilot study was to measure the stability of clinically successful implants in partially edentulous patients after 1 year of loading and to study the influence of jaw, anterior/posterior position, implant length, and marginal bone level on implant stability. MATERIALS AND METHODS: Fourteen partially edentulous patients previously treated with 45 implants were subjected to clinical and radiographic evaluations and RFA measurements using Osstell (Integration Diagnostics, Savedalen, Sweden) after 1 year of loading. RESULTS: All 45 implants were stable, and implant stability levels were in the range of 57 to 82 ISQ units with a mean of 69 +/- 6.5 ISQ after 1 year of loading. Mandibular implants were more stable than were maxillary ones. There were no differences between anterior and posterior implants. No correlation could be found between implant length and stability. Only minor marginal bone resorption was observed. CONCLUSIONS: The results from this limited material showed that successfully integrated implants have ISQ levels from 57 to 82 ISQ with a mean of 69 ISQ after 1 year of loading. Mandibular implants are more stable than are maxillary ones. High implant stability can be achieved with short implants and placement in posterior regions.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".