Neoadjuvant treatments for locally advanced, resectable esophageal cancer: A network meta‐analysis
Bibliographic record
Abstract
The relative survival benefits and postoperative mortality among the different types of neoadjuvant treatments (such as chemotherapy only, radiotherapy only or chemoradiotherapy) for esophageal cancer patients are not well established. To evaluate the relative efficacy and safety of neoadjuvant therapies in resectable esophageal cancer, a Bayesian network meta-analysis was performed. MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials were searched for publications up to May 2016. ASCO and ASTRO annual meeting abstracts were also searched up to the 2015 conferences. Randomized controlled trials that compared at least two of the following treatments for resectable esophageal cancer were included: surgery alone, surgery preceded by neoadjuvant chemotherapy, neoadjuvant radiotherapy or neoadjuvant chemoradiotherapy. The primary outcome assessed from the trials was overall survival. Thirty-one randomized controlled trials involving 5496 patients were included in the quantitative analysis. The network meta-analysis showed that neoadjuvant chemoradiotherapy improved overall survival when compared to all other treatments including surgery alone (HR 0.75, 95% CR 0.67-0.85), neoadjuvant chemotherapy (HR 0.83. 95% CR 0.70-0.96) and neoadjuvant radiotherapy (HR 0.82, 95% CR 0.67-0.99). However, the risk of postoperative mortality increased when comparing neoadjuvant chemoradiotherapy to either surgery alone (RR 1.46, 95% CR 1.00-2.14) or to neoadjuvant chemotherapy (RR 1.58, 95% CR 1.00-2.49). In conclusion, neoadjuvant chemoradiotherapy improves overall survival but may also increase the risk of postoperative mortality in patients locally advanced resectable esophageal carcinoma.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.045 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".