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Record W6958044372 · doi:10.60692/9dvd7-rs595

Effect of primary tumor resection on survival in patients with asymptomatic unresectable metastatic colorectal cancer: a systematic review and meta-analysis

2022· article· en· W6958044372 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsColorectal cancerAsymptomaticPrimary tumorMeta-analysisRandomized controlled trialOverall survivalChemotherapyRetrospective cohort study

Abstract

fetched live from OpenAlex

It remains controversial whether primary tumor resection (PTR) improves survival in patients with asymptomatic, unresectable metastatic colorectal cancer (mCRC). Therefore, we conducted a meta-analysis to assess the latest evidence on clinical outcomes. We systematically searched PubMed, Web of Science, Cochrane Library, and Embase databases for eligible studies published between database inception and May 2022. RevMan 5.4 and Stata 16.0 were used for the meta-analysis. A total of nine studies were included, including four randomized controlled trials (RCTs) and five retrospective cohort studies. Meta-analysis showed that overall survival (OS) [HR = 0.89, 95%CI (0.74, 1.06), P = 0.19] and progression-free survival (PFS) [HR = 0.87, 95%CI (0.71, 1.06), P = 0.17] were not significantly different between the PTR and non-PTR groups. In the subgroup analysis, all subgroups showed no significant difference in OS between the two groups. PTR may not provide additional survival benefits over chemotherapy in asymptomatic, unresectable mCRC patients. However, in view of the limitations of this study, more well-designed RCTs are needed to validate our conclusions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0160.039
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations0
Published2022
Admission routes1
Has abstractyes

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