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Record W1572543248 · doi:10.3892/mco.2015.561

Association of asthma with the risk of acute leukemia and non-Hodgkin lymphoma

2015· article· en· W1572543248 on OpenAlexaboutno aff
Min‐Hang Zhou, Qingming Yang

Bibliographic record

VenueMolecular and Clinical Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOdds ratioInternal medicineAsthmaIncidence (geometry)LymphomaConfidence intervalMeta-analysisCancerOncology

Abstract

fetched live from OpenAlex

An increasing incidence of hematological malignancies has been observed in children and adults worldwide over the last few decades. Asthma is a common chronic inflammatory disease. The aim of the present meta-analysis was to evaluate the potential association between a history of asthma and the risk of acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML) and non-Hodgkin lymphoma (NHL). A literature search was performed through PubMed and the Cochrane Database of Systematic Reviews and the Newcastle-Ottawa Scale was used to evaluate the quality of the selected studies. The I2 index was used to evaluate heterogeneity and the outcome was measured as the odds ratio (OR) by the random-effects model. A total of 16 case‑control studies were included. All the studies were of high quality. The OR for ALL was 0.90 [95% confidence interval (CI): 0.68‑1.19; P=0.45; I2=79%]. The OR for AML was 0.85 (95% CI: 0.67‑1.08; P=0.19; I2=8%). The OR for NHL was 0.91 (95% CI: 0.83‑1.00; P=0.05; I2=0%). Asthma was found to be inversely associated with the risk of NHL. A negative trend of association of asthma with ALL and AML was also observed. However, additional large prospective studies are required 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.336
Teacher spread0.322 · 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 designObservational
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

Citations13
Published2015
Admission routes1
Has abstractyes

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