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Record W2507880004 · doi:10.1038/ajg.2016.45

Editorial: Smoking Cessation for Crohn’s Disease: Clearing the Haze

2016· editorial· en· W2507880004 on OpenAlexaff
Gilaad G. Kaplan

Bibliographic record

VenueThe American Journal of Gastroenterology · 2016
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineSmoking cessationContext (archaeology)DiseaseCrohn's diseaseInternal medicineProspective cohort studyPathology

Abstract

fetched live from OpenAlex

The TABACROHN Study Group conducted a multicenter prospective cohort study, demonstrating that smoking cessation improved the prognosis of Crohn's disease. Patients who continued to smoke were 50% more likely to relapse compared with non-smokers. Smoking cessation reduced the risk of flaring, regardless of exposure to anti-tumor necrosis factor agents. Despite the evidence that smoking cessation is beneficial, many patients do not quit smoking after their diagnosis of Crohn's disease. Lack of awareness, physical addiction, and social context of smoking inhibit smoking cessation. In spite of this, comprehensive smoking cessation programs have been shown to be effective and reduce costs.

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)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.012
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0060.005
Open science0.0040.001
Research integrity0.0120.019
Insufficient payload (model declined to judge)0.0110.012

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 head0.005
GPT teacher head0.255
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations3
Published2016
Admission routes1
Has abstractyes

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