Dentition, nutritional status and adequacy of dietary intake in treatment naïve head and neck cancer patients
Bibliographic record
Abstract
ObjectivesTo examine the relationship of reduced numbers of occluding teeth and dietary intake (DI), nutrition impact symptoms (NIS), and weight loss (WL) in head and neck cancer (HNC) patients.MethodsAs a part of the standard of care, treatment-naïve HNC patients (n = 104) completed dental evaluation (number of teeth, total anterior/posterior occlusal teeth, Eichner Index (EI) classification), WL, DI questionnaire and HNC Symptom Checklist©. Descriptive statistics (Kruskal-Wallis, Fisher-exact, χ2 tests) and (uni-) multi-variable logistic regression.ResultsOverall, 42, 45 and 13% of patients were in EI-class A, B and C with a median of 8, 3, and 0 total posterior occlusal teeth. EI-class B/C patients were older, more likely to have impaired DI (OR = 3.88; 95%CI:1.63–9.26; P = 0.002) and reported interference with DI by 11 NIS (p < 0.05). DI was, however, reported as unimpaired in 77, 49 and 39% of patients in EI-class A, B and C, respectively. The subset of EI-class B/C patients with impaired DI, had more NIS interference with DI (P < 0.05; difficulty chewing, pain, early satiety, lack of energy); EI-class C patients additionally had dry mouth, thick saliva and dysphagia (P < 0.05). In logistic regression, EI-classes B/C patients with reduced (vs unimpaired) DI were more likely to have ≥5% WL (OR = 10.1; 95%CI:2.0–50.0), higher NIS interference (range OR 4.3–10.7).ConclusionsMore than half of these HNC patients had reduced numbers of occlusal teeth or were edentulous. EI-class B/C patients did not necessarily have impaired DI, however the combination of EI-class B/C and a constellation of NIS, associated with reduced DI.Clinical significanceTreatment naïve head and neck cancer (HNC) patients with reduced occlusal and masticatory performance (Eichner Index B/C) and reduced dietary intake are at high risk for weight loss. Identifying HNC patients at risk may improve their oral health, dietary intake and reduce their risk of weight loss.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".