The role of adjuvant radiotherapy after surgery in early-stage tongue carcinoma. A systematic review and meta-analysis
Bibliographic record
Abstract
Study designs to be included Retrospective studies or randomized controlled trials.Eligibility criteria (1) Tongue cancer confirmed by pathological diagnosis; (2) The stage of patients was T1-2N0M0; (3) Providing survival data, including hazard ratio (HR) and 95% confidence interval (CI) measurements for OS, RFS, DFS or PFS, or providing Kaplan-Meier curves based on post-operative radiotherapy (PORT) and surgery only.Information sources We use the four databases of PubMed, Cochrane Library and Web of Science and Chinese databases, a systematic literature search was conducted in October, 2023.Main outcome(s) Survival data, including hazard ratio (HR) and 95% confidence interval (CI) measurements for OS, LRFS, DFS or PFS. Quality assessment / Risk of bias analysisQuality assessment was performed using the Newcastle-Ottawa quality assessment scale (NOS) or The Cochrane ROB.NOS criteria scores range from 0 (lowest) to 9 (highest), and a NOS score ≥6 is considered a high-quality study. Strategy of data synthesisThe statistical analysis was performed using Stata 15.0.It involved calculating the correlations between treatment measures and OS, RFS, PFS, or DFS.If P＜0.05 and I² >50%, it indicated high heterogeneity, and a random-effects model was applied.Otherwise, a fixed-effects model was used.Additionally, a s e n s i t i v i t y a n a l y s i s w a s c o n d u c t e d b y systematically excluding individual studies in order to evaluate the robustness of the meta-analysis.P ＜0.05 was considered statistically significant. Subgroup analysis None. Sensitivity analysis None.Country(ies) involved China.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".