The Influence of Tactile Perception on Classification of Bone Tissue at Dental Implant Insertion
Bibliographic record
Abstract
BACKGROUND: Various ways of using the Lekholm and Zarb (L&Z) classification have added to the lack of scientific evidence of the effectiveness of this clinical method in the evaluation of implant treatment. PURPOSE: The study aims to assess subjective jawbone classifications in patients referred for implant treatment, using L&Z classification with and without surgeon's hand perception at implant insertion. The association between bone type classifications and quantitative parameters of primary implant stability was also assessed. MATERIALS AND METHODS: One hundred thirty-five implants were inserted using conventional loading protocol. Three surgeons classified bone quality at implant sites using two methods: one based on periapical and panoramic images (modified L&Z) and one based on the same images associated with the surgeon's tactile perception during drilling (original L&Z). Peak insertion torque and implant stability quotient (ISQ) were recorded. RESULTS: The modified and original L&Z were strongly correlated (rho = 0.79; p < .001); Wilcoxon signed-rank test showed no significant difference in the distribution of bone type classification between pairs using the two methods (p = .538). Spearman correlation tested the association between primary stability parameters and bone type classifications (-0.34 to -0.57 [p < .001]). CONCLUSIONS: Tactile surgical perception has a minor influence on rating of subjective bone type for dental implant treatment using the L&Z classification.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| 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 | 0.000 |
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".