Pre-irradiation dental care: Ready-to-use templates for oropharyngeal cancers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it