Comparison of tooth loss between intensity-modulated and conventional radiotherapy in head and neck cancer patients.
Bibliographic record
Abstract
OBJECTIVE: Advanced radiotherapy (RT) such as intensity-modulated radiotherapy (IMRT) has become more common in the management of head and neck cancer (HNC). IMRT includes focused target volume coverage while sparing salivary tissues to protect function. However, the long-term effects on dentition after IMRT are not well established. This investigation sought to understand dental status by comparing tooth loss after IMRT and conventional RT in HNC patients. METHODS: A retrospective chart review was conducted on individuals who received IMRT or conventional RT (± surgery, ± chemotherapy) for oropharyngeal, oral cavity, and nasopharyngeal cancer between 2000 and 2010 at the Institute for Reconstructive Sciences in Medicine. Tooth loss, the primary outcome measure, was assessed using intraoral photographs, radiographs, and clinical records. The influence of patient demographics on tooth loss was assessed as well. RESULTS: Eighty-six patients were eligible for review at baseline; 44 received IMRT and 42 received conventional RT. Twenty-four had data collected up to 2 years after RT. After adjusting for baseline number of teeth, no significant differences were found between groups 2 years after RT using repeated measures analysis of covariance (p = .079). The site of disease was significantly different between groups. CONCLUSION: No statistically significant differences in tooth loss between RT groups were found 2 years after RT; however, trends in the data suggest that tooth loss increased each year after RT. The early findings need to be viewed with caution as data beyond 3 to 5 years and a larger sample size are needed to understand the dental effects after advanced RT.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".