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Record W4290613698 · doi:10.1186/s13223-022-00708-9

Association between constipation and the development of asthma: a meta-analysis

2022· article· en· W4290613698 on OpenAlexvenueno aff
Lu Liu, Xiangli Zhang, Zhengdong Jiang, Guizuo Wang, Hua Wu, Ruilin Chen, Yongqing Zhang, Manxiang Li, Shu-Mei Yang

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2022
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsAsthmaAssociation (psychology)Meta-analysisMedicineConstipationPsychologyInternal medicinePsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: Constipation has been hypothesized to be associated with the increased risk of wheezing or asthma. However, the relation remains a subject of debate. We conducted this meta-analysis to assess whether constipation influences the risk of wheezing/asthma. METHODS: PubMed, Embase, and Web of Science were systematically searched for studies published between 1955 and January 2022. Two reviewers independently extracted data and assessed the quality of each study. Results were pooled using fixed-effects models or random-effects models as appropriate. RESULTS: In total, 3 original articles with 178,661 participants, which met the criteria, were included in this meta-analysis. Constipation was associated with an increased risk of wheezing/asthma in later life (RR = 2.02, 95% CI = 1.24-3.29, P < 0.01). CONCLUSIONS: The meta-analysis suggests an association between constipation and the subsequent development of wheezing/asthma. Well-designed and highly standardized prospective studies that adequately address concerns for potential confounding factors are required to validate the risk identified in our current meta-analysis.

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)0.013
metaresearch head score (Gemma)0.025
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 score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0160.063
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0020.002
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 head0.058
GPT teacher head0.328
Teacher spread0.270 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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