Active Tactile Sensibility of Single‐Tooth Implants versus Natural Dentition: A Split‐Mouth Double‐Blind Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Unlike passive sensitivity of implants/teeth that is assessed more, only three controversial studies have compared active tactile sensibility (ATS) of implants and teeth. PURPOSE: We aimed to explore the difference between the ATS of teeth and single-tooth implants. METHODS: The ATS of single-tooth implants and contralateral teeth was measured in 25 patients after they bit on gold and placebo foils 0- to 70-μm thick, each for five times, in a random order blinded to patients and assessor, carried out at two sessions. Based on the experimental range of 0 μm (mock trials) to 70 μm, the sigmoid shape of psychometric curve was estimated to locate the 50% values as the ATS thresholds for each tooth or implant. ATS Data were analyzed using paired and unpaired t-tests and multiple linear regression (α = 0.05, β ≤ 0.1). Also, equivalence testing approach was used to assess semi-objectively the clinical significance. RESULTS: Average ATS values for teeth and implants were 21.4 ± 6.55 μm and 30.0 ± 7.55 μm, respectively (p = .0001 [paired t-test]). None of the geometric characteristics of implants nor duration of implant in function were correlated with the ATS (p > .4 [regression]). Age was positively associated with the ATS of both implants and teeth (p ≤ .019 [regression]). Tooth ATS (but not implant ATS) was significantly higher in males compared with females (p = .050 [unpaired t-test]), which contributed to a generalizable tooth-implant difference higher than 8-μm clinical equivalence margin in females. The ATS was not significantly different between arches or between anterior/posterior regions (p > .6). CONCLUSION: There was a slight but statistically significant difference between implant and tooth tactile sensitivities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".