Prognostic role of plasma Epstein-Barr virus DNA load for nasopharyngeal carcinoma: a meta-analysis
Bibliographic record
Abstract
PURPOSE: Predicting prognosis and treatment outcomes for patients with for nasopharyngeal carcinoma (NPC) has been difficult due to the heterogeneous nature of the disease This study aimed to evaluate pretreatment copy number of plasma Epstein-Barr virus (EBV) DNA as an outcome marker for survival in NPC. METHODS: MEDLINE, CENTRAL and Embase databases were searched until April 7, 2015. Included studies were randomized controlled trials, two-arm prospective studies, or retrospective studies in patients with newly diagnosed NPC. The primary outcome was overall survival and secondary outcomes were progression-free, relapse-free, disease-free and distant metastasis-free survival. Sensitivity, quality and publication bias assessments were performed. RESULTS: Sixteen studies were included in the meta-analysis, with a total of 7698 patients. For overall survival, pooled HR was 3.005 (95% confidence interval [CI] = 2.245-4.022; P < 0.001), indicating that higher levels of EBV DNA were associated with a greater risk of death. Pooled estimates for relapse-free, disease-free, progression-free and distant metastasis-free survival indicated that higher levels of EBV DNA were associated with an increased risk of relapse, disease recurrence, disease progression and distant metastasis in comparison with lower levels of EBV DNA (P values < 0.001). CONCLUSION: This meta-analysis found that high EBV DNA levels indicate poor prognosis and reduced long-term survival in patients with newly diagnosed NPC; hence, EBV DNA levels are highly prognostic of survival in patients with NPC. None of the included studies used the WHO standard for EBV DNA measurement, indicating a greater need for harmonization in future studies.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.063 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".