Comment on Regarding Manuscript “Liver Resection Versus Local Ablation Therapies for Hepatocellular Carcinoma Within the Milan Criteria: A Systemic Review and Meta-analysis”
Bibliographic record
Abstract
To the Editor: Shin et al1 reported a total of 7 randomized controlled trials (RCTs) and 18 matched NRTs, involving 2865 patients with HCC in the liver resection group and 2764 patients in the local ablation therapy group to compare the oncologic outcomes. We appreciate the writing intention of this meta-analysis. However, several methodological issues in the article are worthy of comment. The original documents selected by the meta-analysis have a large extent of publication bias, but the author has no relevant explanation for the publication bias that may occur in the article. In fact, the risk of bias should be part of the conduct and reporting of any systematic review,2 as the PRISMA statement recommended and required. The literature included in this paper includes RCT and cohort studies. Therefore, this study would be more convincing if the results of the new Cochrane risk of bias tool to review the risk of RCT bias and the results of the Newcastle-Ottawa Scale review of the cohort study are added to the content of this article.3–5 In addition, there are no funnel plots or other methods to detect publication bias, which also should be reflected in the meta-analysis. If the author fails to evaluate the publication bias due to other objective reasons (such as too little research content included), an appropriate explanation should be made in the corresponding part of the article.
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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 | 0.027 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.026 | 0.024 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".