Microvascular changes in radiation-induced oral mucositis.
Bibliographic record
Abstract
BACKGROUND: Mucositis is one of the most debilitating side effects of head and neck cancer therapy and is currently believed to arise from an inflammatory cascade leading to cellular damage. However, no effective treatment has been identified despite extensive attempts with anti-inflammatory medications. OBJECTIVE: To compare real-time microvascular inflammatory changes with oral mucositis levels in patients undergoing radiotherapy or chemoradiotherapy for head and neck tumours. DESIGN: Prospective, longitudinal, cohort, observational study. SETTING: Regional cancer program. METHODS: Twenty patients with head and neck tumours were assessed on a weekly basis throughout the course of radiotherapy. Levels of mucositis were graded objectively using the Oral Mucositis Assessment Scale and subjectively using a patient symptom questionnaire. Video imaging of the sublingual microcirculation was obtained using orthogonal polarized spectral imaging to quantify inflammatory markers such as microcirculatory velocity, white blood cell margination, and extravasation. RESULTS: Despite very high levels of objective and subjective mucositis, inflammatory changes were not present in the microcirculation. CONCLUSIONS: Typical microvascular inflammatory changes are not demonstrated in radiation-induced mucositis. These findings contradict the currently proposed mechanism of mucosal damage and may therefore have important implications in the development of novel therapeutic interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".