Meta-analysis of preoperative CALLY index for predicting the prognosis of cancer
Bibliographic record
Abstract
OBJECTIVE: This study aims to evaluate the predictive value of the CALLY index in cancer prognosis via systematic review and meta-analysis. METHODS: PubMed, Web of Science, Embase, and Cochrane were searched up to November 2024. Study quality was evaluated using the Newcastle-Ottawa Scale (NOS), and meta-analysis was performed with STATA 17.0. RESULTS: Among 21 cohort studies,the findings indicated that, regarding overall survival (OS), a low CALLY index was correlated with a 113% elevated likelihood of all-cause mortality compared to those with a higher CALLY index (risk ratio [RR] = 0.47, 95% confidence intervals [95%CI]: 0.42-0.53). An 85% elevated risk of disease-free survival (DFS) and relapse-free survival (RFS) was observed in individuals with a low CALLY index (pooled RR = 0.54, 95%CI: 0.46-0.63). Moreover, a lower CALLY index was correlated with a significantly greater tumor burden (standardized mean difference (SMD) = -0.64, 95%CI: -0.76-0.52). The stage-specific analysis demonstrated that a low CALLY index significantly increased the risk of cancer progression by 54% in individuals at stage II (RR = 0.65, 95%CI: 0.43-0.98) and by 67% in individuals at stage III (RR = 0.60, 95%CI: 0.43-0.86). CONCLUSION: The CALLY index independently predicts adverse cancer outcomes.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.045 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".