MétaCan
Menu
Back to cohort
Record W3033474187 · doi:10.1097/md.0000000000020595

Pretreatment C-reactive protein/albumin ratio for predicting overall survival in pancreatic cancer

2020· review· en· W3033474187 on OpenAlexaboutno aff
Ye Zang, Yu Fan, Zhenjun Gao

Bibliographic record

VenueMedicine · 2020
Typereview
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePancreatic cancerHazard ratioConfidence intervalInternal medicineOncologyProportional hazards modelSubgroup analysisAlbuminCancerRetrospective cohort studyMeta-analysisGastroenterology

Abstract

fetched live from OpenAlex

BACKGROUND: Inconsistent findings have been reported regarding the association of C-reactive protein to albumin ratio (CAR) with survival outcome in patients with pancreatic cancer. We conducted the current meta-analysis to assess the prognostic utility of elevated baseline CAR in predicting overall survival (OS) in pancreatic cancer patients. METHODS: A comprehensively literature search was performed in the PubMed and Embase database until February 10, 2019. Studies evaluating the association between pretreatment CAR and OS among pancreatic cancer were selected. Study quality was evaluated by using the Newcastle-Ottawa Scale. RESULTS: Nine retrospective studies involving 1534 pancreatic cancer patients were identified. A meta-analysis using a random-effect model indicated that elevated CAR was associated with poor OS (hazard ratio 1.98; 95% confidence interval 1.58-2.48). Subgroup analysis produced similar prognostic values for OS in different geographical regions, sample sizes, thresholds of CAR, treating methods, and Newcastle-Ottawa Scale points. CONCLUSION: Elevated pretreatment CAR may independently predict poor OS in pancreatic cancer patients. Pretreatment CAR is possibly a simple and cost-effective blood-derived indicator for predicting survival outcome in patients with pancreatic cancer.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.363
Teacher spread0.306 · 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.

Study designNot applicable
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

Citations29
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMedicineSame topicInflammatory Biomarkers in Disease PrognosisFrench-language works237,207