Submandibular Gland Transfer: A New Method of Preventing Radiation‐Induced Xerostomia
Bibliographic record
Abstract
OBJECTIVE: Radiation-induced xerostomia is a significant morbidity of radiation therapy in the management of patients with head and neck cancers. We have recently reported a method of transfer of one submandibular gland to the submental space in a small pilot series of eligible surgical patients. The submental space was shielded during postoperative radiation therapy. The transferred gland continued to function after the completion of radiation therapy and none of the patients developed xerostomia. The purpose of this article is to present the technique of submandibular gland transfer in detail and to evaluate the postoperative survival and function of the transferred submandibular glands. DESIGN: Prospective clinical trial. METHODS: The submandibular gland was transferred on eligible patients as part of their surgical intervention. The patients were followed clinically, with salivary flow and radioisotope studies. RESULTS: We performed the surgical transfer of the submandibular salivary gland in 24 of 25 patients placed on the protocol. All the glands survived transfer and functioned well postoperatively as demonstrated on the salivary flow and the radioisotope studies. The surgical transfer was relatively simple and added 45 minutes to the surgical procedure. There were no complications attributed to the submandibular gland transfer. CONCLUSIONS: We have successfully demonstrated that the submandibular gland can be surgically transferred to the submental space with its function preserved. The gland seems to continue functioning even after radiation therapy with the appropriate shielding. This surgical transfer procedure has the potential to change the way we currently manage patients with head and neck cancer.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".