The Experience of Head and Neck Cancer Patients With a Percutaneous Endoscopic Gastrostomy Tube at a Canadian Cancer Center
Bibliographic record
Abstract
BACKGROUND: Many patients with advanced head and neck cancer become unable to obtain sufficient nutrition and hydration orally, leading to considerable weight loss and compromised clinical outcomes. The percutaneous endoscopic gastrostomy (PEG) tube is ideal for this population who require longer term nutrition support due to the effects of cancer treatment. Although clinical experts at the Odette Cancer Centre (OCC) report positive patient feedback with PEG tubes, there is debate in the literature regarding the associated quality of life (QoL). The study objective was to learn about the experience of patients living with a PEG tube. MATERIALS AND METHODS: A neutral questionnaire with closed- and open-ended questions was developed, tested, and used to collect data. Quantitative data were analyzed using descriptive statistics to determine whether the patients' experiences were positive/neutral or negative. Qualitative data were assessed for common themes, and frequency was counted. RESULTS: Of the 51 participants, 84% felt the PEG tube had a positive/neutral effect on their QoL. Ninety percent felt that the PEG tube was "very much" or "quite a bit" worthwhile. In addition, 96% would recommend it to another patient. The 11 questions reflecting domains of QoL affected by living with a PEG tube were answered positively or neutrally at least 71% of the time. CONCLUSION: Results indicate that the patient experience with the PEG tube is generally positive or neutral, thus demonstrating a different outcome than recent literature. This study will help improve understanding regarding the experience of living with a PEG tube from the patient perspective.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".