MétaCan
Menu
Back to cohort
Record W2018787659 · doi:10.1136/ebm.7.4.114

Growth rate was greater with fluticasone propionate than with beclomethasone dipropionate in children with chronic asthma

2002· article· en· W2018787659 on OpenAlexaff
FM Ducharme

Bibliographic record

VenueEvidence-Based Medicine · 2002
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcGill University
Fundersnot available
KeywordsFluticasone propionateMedicineAsthmaFluticasoneBudesonidePediatricsMorningInternal medicine

Abstract

fetched live from OpenAlex

(2001) Arch Pediatr Adolesc Med 155, 1248; de Benedictis FM, Teper A, Green RJ, et al, for the International Study Group. . Effects of 2 inhaled corticosteroids on growth. Results of a randomized controlled trial. . Nov; . : . –54 . [OpenUrl][1][PubMed][2][Web of Science][3] QUESTION: In children with chronic asthma, what are the effects of fluticasone propionate compared with those of beclomethasone dipropionate on growth rates? Randomised (allocation concealed*), blinded (unclear),* controlled trial with 12 months of follow up. 32 centres in 7 countries: The Netherlands, Hungary, Italy, Poland, Argentina, Chile, and South Africa. 343 children who were 4 to 11 years of age (mean age 8y, 72% boys); had a sexual maturity rating of Tanner stage 1; required treatment with fluticasone propionate, 100 to 200 μg/day, or beclomethasone dipropionate or budesonide, 200 to 500 μg/day, for ≥ 8 weeks before study entry at a constant dosage for ≥ 4 weeks before the run-in period; had a mean morning peak expiratory flow rate (PEFR) during the last 7 days of the run-in period of ≤ 85% of their maximum achievable response after using a metered dose inhaler containing albuterol sulphate, 400 μg; and had an asthma symptom score ≥ … [1]: {openurl}?query=rft.jtitle%253DArchives%2Bof%2BPediatrics%2Band%2BAdolescent%2BMedicine%26rft.stitle%253DArch%2BPediatr%2BAdolesc%2BMed%26rft.issn%253D0002-922X%26rft.aulast%253Dde%2BBenedictis%26rft.auinit1%253DF.%2BM.%26rft.volume%253D155%26rft.issue%253D11%26rft.spage%253D1248%26rft.epage%253D1254%26rft.atitle%253DEffects%2Bof%2B2%2BInhaled%2BCorticosteroids%2Bon%2BGrowth%253A%2BResults%2Bof%2Ba%2BRandomized%2BControlled%2BTrial%26rft_id%253Dinfo%253Apmid%252F11695935%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=11695935&link_type=MED&atom=%2Febmed%2F7%2F4%2F114.atom [3]: /lookup/external-ref?access_num=000172074700012&link_type=ISI

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.251
Teacher spread0.225 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
Domainnot available
GenreCommentary · Empirical

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

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueEvidence-Based MedicineSame topicAsthma and respiratory diseasesFrench-language works237,207