Esophageal microbiota composition and outcome of esophageal cancer treatment: a systematic review
Bibliographic record
Abstract
BACKGROUND: The role of esophageal microbiota in esophageal cancer treatment is gaining renewed interest, largely driven by novel DNA-based microbiota analysis techniques. The aim of this systematic review is to provide an overview of current literature on the possible association between esophageal microbiota and outcome of esophageal cancer treatment, including tumor response to (neo)adjuvant chemo(radio)therapy, short-term surgery-related complications, and long-term oncological outcome. METHODS: A systematic review of literature was performed, bibliographic databases were searched and relevant articles were selected by two independent researchers. The Newcastle-Ottawa scale was used to estimate the quality of included studies. RESULTS: The search yielded 1303 articles, after selection and cross-referencing, five articles were included for qualitative synthesis and four studies were considered of good quality. Two articles addressed tumor response to neoadjuvant chemotherapy and described a correlation between high intratumoral Fusobacterium nucleatum levels and a poor response. One study assessed surgery-related complications, in which no direct association between esophageal microbiota and occurrence of complications was observed. Three studies described a correlation between shortened survival and high levels of intratumoral F. nucleatum, a low abundance of Proteobacteria and high abundances of Prevotella and Streptococcus species. CONCLUSIONS: Current evidence points towards an association between esophageal microbiota and outcome of esophageal cancer treatment and justifies further research. Whether screening of the individual esophageal microbiota can be used to identify and select patients with a predisposition for adverse outcome needs to be further investigated. This could lead to the development of microbiota-based interventions to optimize esophageal microbiota composition, thereby improving outcome of patients with esophageal cancer.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".