MétaCan
Menu
Back to cohort
Record W2065220885 · doi:10.1097/inf.0b013e3182443fec

Long-term Effects of Clearing Helicobacter pylori on Growth in School-age Children

2012· article· en· W2065220885 on OpenAlexaff
Robertino M. Mera, Luis Eduardo Bravo, Karen J. Goodman, María Clara Yépez, Pelayo Correa

Bibliographic record

VenueThe Pediatric Infectious Disease Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsUniversity of Alberta
FundersNational Cancer Institute
KeywordsHelicobacter pyloriClearingTerm (time)PsychologyDevelopmental psychologyMedicineGastroenterologyEconomicsPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: A new Helicobacter pylori infection affects growth velocity negatively, and clearing the infection produces a small significant rebound, but it is not known whether height and weight in children are impacted over the long term. METHODS: We investigated 295 school-age children followed in 2 cohorts, treated (150) and untreated (145), from 2004 for 3.7 years with 1105 child-years of observation. Follow-up intervals were 3 months for anthropometry measurements and 6 months for H. pylori status ascertained by urea breath test. Height in centimeters and weight in kilograms were analyzed using growth models. RESULTS: A multivariate mixed model that adjusted for age, sex, father's education, and number of siblings found no significant differences in height or weight at baseline by H. pylori status. The same model showed a significant impact of clearing H. pylori across time, with increasing significant differences in average height and weight as the follow-up progressed. CONCLUSIONS: Children who were always negative or who cleared the infection grew significantly faster than those who stayed positive after adjusting for other covariates. This study suggests that school-age children's growth benefits from being treated for H. pylori infection.

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)0.001
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 score0.003
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.242
Teacher spread0.234 · 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

Citations39
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe Pediatric Infectious Disease JournalSame topicHelicobacter pylori-related gastroenterology studiesFrench-language works237,207