Association of hospital volume and long-term survival after esophagectomy: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: It remains controversial whether esophageal cancer patients may benefit from esophagectomy in specialized high-volume hospitals. Here, the effect of hospital volume on overall survival (OS) of esophageal cancer patients post esophagectomy was assessed. Methods: PubMed, Embase, and Cochrane Library were systematically searched for relevant published articles between January 1990 and May 2022. The primary outcome was OS after esophagectomy in high- vs. low-volume hospitals. Random effect models were applied for all meta-analyses. Subgroup analysis were performed based on volume grouping, sample size, study country, year of publication, follow-up or study quality. Sensitivity analyses were conducted using the leave-one-out method. The Newcastle-Ottawa Scale was used to assess the study quality. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidance, and was registered (identifier: INPLASY202270023). Results: < 0.01). Next, we conducted subgroup analysis based on volume grouping category, consistent results were found that high-volume hospitals significantly improved OS after esophagectomy than their low-volume counterparts. Subgroup analysis and sensitivity analyses further confirmed that all the results were robust. Conclusions: Esophageal cancer should be centralized in high-volume hospitals.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| 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".