Role of Clinician's Experience and Implant Design on Implant Stability. An Ex Vivo Study in Artificial Soft Bones
Bibliographic record
Abstract
OBJECTIVES: Clinical experience in implant placement is important in order to prevent implant failures. However, the implant design affects the primary implant stability (PS) especially in poor quality bones. Therefore, the aim of this study was to compare the effect of clinician surgical experience on PS, when placing different type of implant designs. METHODS: A total of 180 implants (90 parallel walled-P and 90 tapered-T) were placed in freshly slaughtered cow ribs. Bone quality was evaluated by two examiners during surgery and considered as 'type IV' bone. Implants (ø 5 mm, length: 15 mm, Osseotite, BIOMET 3i, Palm Beach Gardens, FL, USA) were placed by three different clinicians (master/I, good/II, non-experienced/III, under direct supervision of a manufacturer representative; 30 implants/group). An independent observer assessed the accuracy of placement by resonance frequency analysis (RFA) with implant stability quotient (ISQ) values. Two-way analysis of variance (ANOVA) and Tukey's post hoc test were used to detect the surgical experience of the clinicians and their interaction and effects of implant design on the PS. RESULTS: All implants were mechanically stable. The mean ISQ values were: 49.57(± 18.49) for the P-implants and 67.07(± 8.79) for the T-implants. The two-way ANOVA showed significant effects of implant design (p < .0001), clinician (p < .0001), and their interaction (p < .0001). The Tukey's multiple comparison test showed significant differences in RFA for the clinician group I/II (p = .015) and highly significant (p < .0001) between I/III and II/III. The P-implants presented (for I, II, and III) mean ISQ values 31.25/49.18/68.17 and the T-implants showed higher ISQ values, 70.15/62.08/68.98, respectively. Clinicians I and II did not show extreme differences for T-implants (p = .016). In contrast, clinician III achieved high ISQ values using P- and T-implants following the exact surgical protocol based on the manufacturer guidelines. T-implants provided high stability for experienced clinicians compared with P-implants. CONCLUSION: T-implants achieved greater PS than the P-implants. All clinicians consistently achieved PS; however, experienced clinicians achieved higher ISQ values with T-implants in poor quality bone.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".