Enhancing Nutrition Support for Esophageal Cancer Patients: Understanding Factors Influencing Feeding Tube Utilization
Bibliographic record
Abstract
Objective: We sought to identify factors that can predict esophageal cancer (EC) patients at high risk of requiring feeding tube insertion. Methods: A retrospective cohort review was conducted, including all patients diagnosed with EC at our cancer center from 2013 to 2018. Multivariate logistic regression was performed comparing the group that required a reactive feeding tube insertion to those who did not require any feeding tube insertion to identify risk factors. Results: A total of 350 patients were included in the study, and 132/350 (38%) patients received a feeding tube. 50 out of 132 (38%) patients had feeding tube inserted reactively. Severe dysphagia (OR 19.9, p < 0.001) at diagnosis and decision to undergo chemotherapy (OR 2.8, p = 0.008) appeared to be predictors for reactive feeding tube insertion. The reactive insertion group had a 7% higher rate of complications relating to feeding tube. Conclusion: Severe dysphagia at diagnosis and undergoing chemotherapy were identified as risk factors for requiring a feeding tube. Ultimately, the aim is to create a predictive tool that utilizes these risks factors to accurate identify high-risk patients who may benefit from prophylactic feeding tube insertion.
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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 | 0.006 |
| 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 | 0.001 |
| 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".