Pre-irradiation dental care: Ready-to-use templates for oropharyngeal cancers
Bibliographic record
Abstract
To develop a tool in order to guide pre-irradiation dental care (PIDC) for patients with oropharyngeal cancers. Osteoradionecrosis of the jaws is a potential complication of radiotherapy (RT) for head and neck cancers. To prevent this complication, PIDC can involve multiple dental extractions as a preventative measure to avoid post-RT complications. However, there is no standardized tool to guide PIDC. From January 2005 to October 2015, 120 head and neck cancer patients were prospectively included in a study investigating dysgeusia after RT. From this cohort, patients were enrolled according to the following inclusion criteria: histopathological confirmation of oropharyngeal squamous cell carcinoma; stage T1-4 N1-3 M0; ≤10 missing teeth. Individual teeth were retrospectively delineated on planning computed tomography and doses to dentition were assessed to generate templates. Thirty-three patients were included. Molars received highest doses with a mean dose of 50 Gy (range; 19–75 Gy). Ipsi-lateral and contralateral wisdom teeth received RT dose superior to 50 Gy in 92% and 56% of cases, respectively. Patients with advanced disease (T4 or N2c-3) received higher mean doses on inferior and ipsi-lateral dental arches compared to other patients (T1-3 N0-2b): 42 Gy vs. 39 Gy and 44 Gy vs. 39 Gy (p < 0.05), respectively. Pre-RT dose distribution templates are an objective way to prepare PIDC. Further studies with a larger cohort are needed to validate these templates.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".