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Record W4417079734 · doi:10.1080/17520363.2025.2600696

Meta-analysis of preoperative CALLY index for predicting the prognosis of cancer

2025· article· en· W4417079734 on OpenAlexaboutno aff
Congying Li, Wenlong Zhou, Xin Zhao, Yanli Sun, Jianfeng Zang, Shujing Wang

Bibliographic record

VenueBiomarkers in Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsCancerIndex (typography)Adverse effectMEDLINE

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.370
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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