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Record W3098370960 · doi:10.1097/mph.0000000000001996

Impact of ABO Blood Group on the Development of Venous Thromboembolism in Children With Cancer: A Systematic Review and Meta-Analysis

2020· review· en· W3098370960 on OpenAlexaff
Jeffrey Lam Shin Cheung, Victor Lam Shin Cheung, Uma H. Athale

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2020
Typereview
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsWestern UniversityHamilton Health SciencesMcMaster Children's Hospital
Fundersnot available
KeywordsMedicineABO blood group systemMeta-analysisOdds ratioCINAHLMEDLINEConfidence intervalInternal medicineCancerPsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Few studies have evaluated the impact of ABO blood group on the risk of venous thromboembolism (VTE) in pediatric populations. We performed a systematic review to determine whether children (0 to 18 y old) with non-O blood group have an increased risk of developing VTE compared with those with O blood group. METHODS: We searched Ovid Medline, Embase, PubMed, Cochrane, Web of Science, and CINAHL online databases from inception to December 2018 to find studies involving blood grouping and VTE. Data was collected regarding patients' underlying diseases, sex, age, ABO blood group, and VTE frequency. A meta-analysis using the random effect model was performed, and heterogeneity was assessed with the I2 value. RESULTS: Among 1280 unique articles identified, 7 studies (3 conference abstracts, 4 peer-reviewed journal articles) involving 609 VTE cases were included in the systematic review. Six studies were eligible for a meta-analysis; all involved patients with cancer. Blood group O was protective against VTE (odds ratio, 0.56; 95% confidence interval, 0.43-0.79). Findings were similar across sensitivity analyses. CONCLUSIONS: This systematic review has documented that O blood group is protective against new-onset VTE in children with cancer. Larger studies across different ethnic backgrounds and disease categories are needed to confirm these findings.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.539
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0210.002
Bibliometrics0.0010.002
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 head0.053
GPT teacher head0.369
Teacher spread0.316 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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