Risk Factors for Esophageal Fistula in Esophageal Cancer Patients Treated with Radiotherapy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Esophageal fistula is a critical and fatal complication of esophageal cancer. The aim of this meta-analysis was to explore the risk factors for esophageal perforation in esophageal cancer patients treated with radiotherapy. METHODS: Data from the PubMed and Embase databases were retrieved for clinical research published between 1990 and 2018. The Newcastle-Ottawa Scale was used to evaluate the quality of the articles. A meta-analysis was performed using the RevMan 5.3 software provided by the Cochrane Collaboration Network. RESULTS: Seventeen articles were eligible for the meta-analysis. In these articles, over 35 risk factors for esophageal fistula formation were described and 17 risk factors were analyzed. Significant differences in the odds of developing an esophageal perforation were found with regard to age (OR 2.34, 95% CI 1.08-5.03, p = 0.001), ulcerative type (OR 2.72, 95% CI 1.43-5.16, p = 0.002), histology (OR 4.16, 95% CI 1.14-15.12, p = 0.03), T stage (OR 2.66, 95% CI 1.44-4.91, p = 0.002), short-term response (OR 2.21, 95% CI 1.06-4.62, p = 0.03), chemotherapy regimen (OR 2.80, 95% CI 1.38-5.68, p = 0.005), and stenosis (OR 2.00, 95% CI 1.03-3.89, p = 0.04). CONCLUSIONS: An age of <60-65 years, ulcerative type, squamous cell cancer, T4 stage, incomplete response, fluorouracil-based regimen, and stenosis were associated with an increased risk of esophageal fistula during or after radiotherapy. However, further, large-scale prospective studies are needed to establish the validity of this associ-ation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".