Confounding factors in the assessment of oral mucositis in head and neck cancer
Bibliographic record
Abstract
Treatment of locally advanced head and neck carcinoma not amenable for surgical resection or resected with high-risk features is usually based on (chemo-)radiation treatment. Oral mucositis represents one of the main side effects of (chemo-)radiation, with an important impact on quality of life and causing approximately 20% of early interruption of treatment, leading to a suboptimal dose administered. Treatment and prevention of oral mucositis have a central role in the therapeutic pathways of head and neck cancer patients but remains quite challenging. Although extensive research is conducted to identify interventions for the management of mucositis, very few interventions had sufficient evidence to generate an international expert consensus. This may be partially explained by confounding factors that could influence the development and assessment of oral mucositis. Little is known about the confounding factors of oral mucositis, which, if not well balanced in an experimental study, could lead to non-solid results. The current paper aims to review the main oral mucositis confounding factors related to head and neck cancer patients.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".