Thiel Embalming Technique: A Valuable Method for Teaching Oral Surgery and Implantology
Bibliographic record
Abstract
BACKGROUND: Because of its high requirements on surgical experience and the need of complete understanding of the anatomy, oral surgery and especially implantology belong to the most demanding procedures in dentistry. Therefore, hands-on courses for oral surgery and implantology are considered a prerequisite to prepare for clinical practice. To achieve teaching conditions as realistic as possible, we used a novel human cadaver embalming method enabling tissue dissection comparable with the living body. METHODS: Thirty cadavers which were offered by the Institute of Anatomy for the purpose of running oral surgery and implantology courses were embalmed in the technique described by Thiel. On each cadaver, dissection of soft and hard tissue and implantological procedures were performed according to a structured protocol by each course participant. The conservation of fine anatomical structures and the suitability of the embalmed tissue for dissecting, drilling, and suturing were observed and photographically documented. RESULTS: By means of the Thiel embalming technique, oral surgery and implantological procedures could be performed under realistic conditions similar to the living body. Due to the conservation procedure, preparations could be carried out without any time limit, always maintaining the same high quality of the tissue. The maxillary sinus membrane, mucosa, bone, and nerves could be exposed and allowed dissecting, drilling, and suturing even after weeks like fresh specimens. CONCLUSION: The Thiel embalming method is a unique technique which is ideally suited to practice and teach oral surgery and implantology on human material.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".