Predicting Nomogram for Severe Oral Mucositis in Patients with Nasopharyngeal Carcinoma during Intensity-Modulated Radiation Therapy: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Oral mucositis is an acute adverse reaction with high incidence during radiotherapy. Severe oral mucositis can seriously affect patients' quality of life and compliance with radiotherapy. The aim of this study was to identify the risk factors for severe oral mucositis and to develop a nomogram for predicting severe oral mucositis in patients with nasopharyngeal carcinoma. METHODS: One hundred and ninety patients with nasopharyngeal carcinoma were retrospectively screened in this study. Least absolute shrinkage and selection operator regression and multivariate logistic regression analyses were performed to identify the best predictors of severe oral mucositis. A nomogram was constructed based on the factors. Finally, the discriminative ability of the nomogram was evaluated. RESULTS: Four independent factors predicting severe oral mucositis were identified: age, N stage, the cycle of induction chemotherapy, and dose-volumetric parameter V40 (%) of oral cavity. The area under the receiver of operating characteristic curve of the nomogram was 0.759 (95% confidence interval: 0.691-0.827). CONCLUSIONS: A predictive nomogram for severe oral mucositis was established and validated in this study. The nomogram provides a reliable and practical model for clinically predicting the probability of severe oral mucositis in patients with nasopharyngeal carcinoma before intensity-modulated radiation therapy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".