A Prospective Cohort Study on the Effect of Intensity-Modulated Radiation Therapy on Head and Neck Cancer Patients’ Quality of Life Using Version 4 of the University of Washington Quality of Life Questionnaire
Bibliographic record
Abstract
Abstract Purpose: The management of head and neck cancer (HNC) patients requires balancing disease control with structural, cosmetic, and functional deficits that can negatively impact quality of life (QoL). Therefore, understanding the physical, emotional and social aspects of QoL of patients throughout their HNC treatment process can help providers better treat this populations of patients in order to improve their QoL. Methods: This was a prospective cohort study of sixty-eight consecutive patients with HNC receiving curative intent RT at Princess Margaret Cancer Centre who had completed the self-administered UW-QoL v4 pre-RT, Mid-RT, 1-month and 6-months post-RT. Results: All scores on the questionnaire decreased mid-RT, with taste and saliva demonstrating the greatest decrease (p <. 05). All scores increased and therefore improved 1-month post-RT and further improved at 6-months post-RT (p <.05). Mean scores did not return to the mean pre-RT levels for the domains swallowing, taste, saliva, and for the physical function composite scores. Conclusions:HNC patients undergoing RT treatment need to be supported and monitored by health care providers for alterations in QoL during and post-RT, especially with respect to their taste and saliva.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".