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Record W4391608799 · doi:10.3389/fonc.2024.1333094

The prognostic value of lymph node ratio for thyroid cancer: a meta-analysis

2024· review· en· W4391608799 on OpenAlexaboutno aff
Yue Hu, Zhiyi Wang, Lishuo Dong, Lu Zhang, Li XiuYang

Bibliographic record

VenueFrontiers in Oncology · 2024
Typereview
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsnot available
FundersChina Academy of Traditional Chinese MedicineChina Academy of Chinese Medical Sciences
KeywordsPublication biasFunnel plotMeta-analysisMedicineThyroid cancerSubgroup analysisInternal medicineOncologyHazard ratioConfidence intervalLymph nodeCancer

Abstract

fetched live from OpenAlex

Background: The prognostic value of lymph node ratio (LNR) has been proved in several cancers. However, the potential of LNR to be a prognostic factor for thyroid cancer has not been validated so far. This article evaluated the prognostic value of LNR for thyroid cancer through a meta-analysis. Methods: A systematic search was conducted for eligible publications that study the prognostic values of LNR for thyroid cancer in the databases of PubMed, EMBASE, Cochrane, and Web of Science up until October 24, 2023. The quality of the eligible studies was evaluated by The Newcastle-Ottawa Assessment Scale of Cohort Study. The effect measure for meta-analysis was Hazard Ratio (HR). Random effect model was used to calculate the pooled HR and 95% confidence intervals. A sensitivity analysis was applied to assess the stability of the results. Subgroup analysis and a meta-regression were performed to explore the source of heterogeneity. And a funnel plot, Begg's and Egger's tests were used to evaluate publication bias. Results: A total of 15,698 patients with thyroid cancer from 24 eligible studies whose quality were relatively high were included. The pooled HR was 4.74 (95% CI:3.67-6.11; P<0.05) and a moderate heterogeneity was shown (I2 = 40.8%). The results of meta-analysis were stable according to the sensitivity analysis. Similar outcome were shown in subgroup analysis that higher LNR was associated with poorer disease-free survival (DFS). Results from meta-regression indicated that a combination of 5 factors including country, treatment, type of thyroid cancer, year and whether studies control factors in design or analysis were the origin of heterogeneity. Conclusion: Higher LNR was correlated to poorer disease free survival in thyroid cancer. LNR could be a potential prognostic indicator for thyroid cancer. More effort should be made to assess the potential of LNR to be included in the risk stratification systems for thyroid cancer. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=477135, identifier CRD42023477135.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.512
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.080
GPT teacher head0.412
Teacher spread0.331 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations6
Published2024
Admission routes1
Has abstractyes

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