Computer<i>‐</i>guided chin harvest: A novel approach for autogenous block harvest from the mandibular symphesis
Bibliographic record
Abstract
BACKGROUND: The introduction of CAD/CAM technology allowed clinicians to carry out complex procedures with a high level of precision and reproducibility and minimize the risk of injury during the procedure. PURPOSE: The aim of the present study is to evaluate the efficacy of the CAD/CAM surgical guide during chin harvesting procedures in reducing the risk of neurosensory damage and patient morbidity compared with the standard technique. MATERIALS AND METHODS: About 20 cases of autogenous block chin harvest were randomly into two groups. The first group received computer-guided chin block harvest while the second group received autogenous block chin harvest using the standard approach. RESULTS: In the guided group, out of the 10 subjects, 2 subjects presented with wound dehiscence which resolved within 1 month. No teeth showed any negative pulp sensitivity results. The pointed-Blunt test and 2 point discrimination tests showed a single case of neurodeficits at 1 week follow-up appointment which resolved within 1 month. In the nonguided group, out of the 10 subjects, 1 case presented with wound dehiscence that resolved completely within 1 month. Pulp vitality test showed negative results in 29.4% of the involved teeth at 1 week which decreased to 9.8% and 3.9% at 1 and 6 months follow-up, respectively. The pointed blunt test revealed 3 subjects with neurodeficits at 1 week, out of which 2 subjects showed persisting symptoms at 6 months follow-up. The 2 point discrimination test showed 3 subjects with neurodeficits, out of which two subjects showed persistent symptoms with no resolution at the 6 months follow-up. CONCLUSION: Within the limits of this study, computer-guided chin harvest shows promising results in the reduction of neurosensory complications following harvesting procedures and presents as a safe alternative to the standard technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".