The Versatile Contact Nd:YAG Laser in Head and Neck Surgery: An in Vivo and Clinical Analysis
Bibliographic record
Abstract
OBJECTIVE: Lasers have been used in otolaryngology as a surgical instrument for more than 25 years, and the CO2 laser has emerged as the most widely employed surgical laser in use today. However, recent technological advances have made the Nd:YAG laser a challenger as an effective photothermal surgical tool. STUDY DESIGN AND METHODS: This is a two-part study. Tissue injury and healing profiles after application of both the CO2 and Nd:YAG lasers are compared using an in vivo rat tongue model. A prospective clinical review based on the experience of 327 operative cases spanning a 7-year interval using the Nd:YAG laser, highlighting its various applications and associated complications, is detailed. RESULTS: Comparable tissue and healing effects were noted with both lasers in the in vivo rat tongue model with no statistical differences. The clinical application of the laser showed wide versatility in the head and neck with a complication rate of 3%. CONCLUSION: The Nd:YAG laser has proved equivalent in tissue damage and healing to the CO2 laser. The Nd:YAG laser has proved itself to be an excellent and perhaps superior laser for use in head and neck surgery.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".