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Record W2523791325 · doi:10.1186/s40064-016-3349-0

Association between ischemic heart disease and colorectal neoplasm: a systematic review and meta-analysis

2016· review· en· W2523791325 on OpenAlexaboutno aff
Yoo Jee Hee, Chang Seok Bang, Gwang Ho Baik, In‐Soo Shin, Ki Tae Suk, Tae Young Park, Dong Joon Kim

Bibliographic record

VenueSpringerPlus · 2016
Typereview
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineColorectal cancerMeta-analysisFunnel plotNeoplasmPublication biasMEDLINEOdds ratioOncologyCochrane LibraryPathologyCancer

Abstract

fetched live from OpenAlex

PURPOSE: Colorectal neoplasm and ischemic heart disease (IHD) share common risk factors. However, clinical guidance about screening or surveillance of colorectal neoplasm in patients with IHD has not been made. The aim of this study was to investigate the relationship between IHD and the development of colorectal neoplasm. METHODS: A systematic literature review was conducted using the core databases (MEDLINE through PubMed, EMBASE, and the Cochrane Library). The data about the association between IHD and the development of colorectal neoplasm were extracted and analyzed using odds ratio (OR). A random effect model was applied. The methodological quality of the enrolled studies was assessed by the Newcastle-Ottawa Scale. Publication bias was evaluated through the funnel plot with trim and fill method, Egger's test, and the rank correlation test. RESULTS: < 0.001). Sensitivity analyses showed consistent results. Publication bias was not detected. CONCLUSION: Patients with IHD is associated with colorectal neoplasm, which warrants screening or surveillance of colorectal neoplasm in this group of patients.

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)0.008
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.020
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.327
Teacher spread0.284 · 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
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

Citations25
Published2016
Admission routes1
Has abstractyes

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