Evaluating the Etiology of Metallic Taste During Head and Neck Cancer Treatments: A Study of Facial and Glossopharyngeal Nerve Interactions
Bibliographic record
Abstract
Metallic Taste (MT) is frequently described during head and neck cancer treatments but very little is known about its etiologies. One hypothesis to explain the MT is the removal of facial nerve inhibition on the glossopharyngeal nerve. Indeed, the decrease of taste afferents mediated by the facial nerve (anterior two-thirds of the tongue) due to cancer or its treatments, would reveal those mediated by the glossopharyngeal nerve (posterior one-third of the tongue) and thus lead to MT perception. The aim of this study was to evaluate the validity of this hypothesis. Selective supraliminar taste tests on the tip and the base of the tongue were regularly performed on 44 patients with head and neck cancers before, during, and after their treatment. Sweet, salty, bitter, sour, and MT were tested. Patients were grouped based on whether they reported experiencing MT or not. 12 patients complained about MT (27.2%), always during the treatment phase. Most of them (83.3%) were treated by surgery and radiotherapy or radiochemotherapy. Supraliminar tastes were altered in every patient, especially during the treatment phase. Test results showed that perceived intensity was significantly reduced in patients reporting MT for salt, sweet and sour. This was observed more on the base of tongue than on the tip of the tongue. MT was significantly linked with mucositis (p=0.027) but with neither candidiasis (p=0.38) nor salivary flow (p=0.63). The hypothesis of removal of facial nerve inhibition on the glossopharyngeal nerve cannot explain MT in head and neck cancer.
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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) | 0.001 | 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".