Factors associated with quality of life in outpatients with head and neck cancer 6 months after diagnosis
Bibliographic record
Abstract
BACKGROUND: Identifying patients with head and neck cancer at greatest risk of poor health-related quality of life (HRQOL) will facilitate screening for such patients and targeted interventions. METHODS: This was a cross-sectional, self-administered survey with medical record review among 65 out-patients with head and neck cancer >6 months from diagnosis and off treatment. RESULTS: Most were men (80%) and white (95%), with a mean age of 60 +/- 13 years. The most prevalent cancer type was squamous cell (88%), site was pharyngeal (40%), and stage was III or IV (80%). Lower total HRQOL was independently associated with gastrostomy (p < .001) and history of radiation therapy (p < .05)(R(2) = 0.27). Certain HRQOL subscales were also independently associated with depression, body mass index, age, and education. CONCLUSIONS: Several factors can be used to identify patients with head and neck cancer at risk for persistent reductions in HRQOL requiring intervention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".