Comparison of Prophylactic Versus Reactive Tube Feeding Approaches on Weight Loss and Unplanned Hospital Admissions in Patients with Head and Neck Cancer Receiving Chemoradiotherapy
Bibliographic record
Abstract
The study’s aim was to compare the unplanned admission rates and nutrition outcomes in patients with head and neck squamous cell cancer (HNSCC) receiving chemoradiotherapy at two different hospitals with different nutrition support approaches. Hospital Site A used prophylactic tube feeding and Site B used reactive tube feeding. Consecutive HNSCC patients receiving chemoradiotherapy with curative intent over six months in 2015 were eligible for this prospective comparative cohort study. Only patients who were classified as at high nutrition risk using validated guidelines were included. Patients’ weight was recorded at the start, end, and 4–6 weeks post treatment to determine percentage weight loss outcomes. Unplanned hospital admissions (for medical or nutrition related reasons) and associated length of stay (LOS) were collected throughout and up to 1-month post treatment. In total, 88 patients were included in the study (site A n = 58; site B n = 30). The mean age was 60 years, 86–90% were male, and predominantly had oropharyngeal cancer. There was no statistical difference between the groups for percentage weight loss at any timepoint, rates of unplanned nutrition related admissions, or LOS. Stepwise logistic analysis showed that being of an older age was predictive of having an unplanned nutrition-related admission. In summary, there was no difference in the rate of unplanned admissions or percentage weight loss for patients with HNSCC managed under the prophylactic versus reactive tube feeding approach. Decision making regarding the choice of feeding tube should be made in consultation with the patient and the multidisciplinary team.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".