Effect of Percutaneous Endoscopic Gastrostomy on Quality of Life after Chemoradiation for Locally Advanced Nasopharyngeal Carcinoma: A Cross-Sectional Study
Bibliographic record
Abstract
(1) Background: Prophylactic percutaneous endoscopic gastrostomy (PEG) maintained nutritional status and improved survival of patients with locally advanced nasopharyngeal carcinoma (LA-NPC). However, the role of PEG in patients’ quality of life (QoL) is still controversial. We aimed to investigate the effect of PEG on the QoL of patients with LA-NPC without progression. (2) Methods: Patients with LA-NPC between 1 June 2010 and 30 June 2014 in Fujian Cancer Hospital were divided into PEG and non-PEG groups. The QoL Questionnaire core 30 (QLQ-C30), incidence of adverse effects, weight, and xerostomia recovery were compared between the two groups of patients without progression as of 30 June 2020. (3) Results: No statistically significant difference in the scores of each QLQ-C30 scale between the two groups (p > 0.05). The incidence of xerostomia was higher in the PEG group than in the non-PEG group (p = 0.044), but the association was not seen after adjusting for gender, age, T, and N stage (OR: 0.902, 95%CI: 0.485−1.680). No significant difference in the incidence of other adverse effects as well as in weight and dry mouth recovery (p > 0.05). (4) Conclusion: PEG seems not to have a detrimental effect on long-term Qol, including the self-reported swallowing function of NPC patients without progressive disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".