Perceptual Changes in the Peri-Implant Soft Tissues Assessed by Directional Cutaneous Kinaesthesia and Graphaesthesia: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: The innervation of skin and oral mucosa plays a major physiological role in exteroception. This innervation is also clinically relevant as sensory changes occur after neurosurgical procedures. PURPOSE: The goal of this study was to compare the perception of mechanical stimuli applied to the buccal mucosa in the vicinity of osseointegrated oral implants with that in the controlateral dentate side. The role of the previously reported increased innervation in the peri-implant soft tissues in the oral sensorimotor function was thus examined. MATERIALS AND METHODS: Seventeen subjects with 20 implants were tested. Directional cutaneous kinaesthesia (DCK) and graphesthesia (G) were performed on the buccal side of the alveolar mucosa before and at planned intervals after implant placement. The observation was pursued until 6 months after the prosthetic rehabilitation. In each subject, the contralateral mucosa served as a control to the implant sites. Average percentages of correct responses in a four-choice task for DCK and a three-choice task for G were calculated. RESULTS: Despite an intersubject variation in both the DCK and G, high intraindividual correlations were found (p < .005). The implant sites showed a significant difference toward the control sites at the four interval test for both tests. For DCK and G, the average of correct responses decreased after abutment connection (i.e., after the implant uncovering surgery) to increase afterwards to reach a level close to, but still lower than, the control sites 3 to 6 months after the prosthetic rehabilitation. CONCLUSION: The DCK and G are simple but reliable sensory tests that can be easily applied in the oral region. This prospective study indicates that tooth loss reduces tactile function compared with implant-supported prostheses. The peri-implant soft tissues could be partially involved in the osseoperception function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".