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Record W2293248869 · doi:10.4081/monaldi.2013.106

Smoking cessation treatment for COPD smokers: the role of pharmacological interventions

2015· review· en· W2293248869 on OpenAlexaff
Carlos A. Jiménez-Ruíz, K.O. Fagerström

Bibliographic record

VenueMonaldi Archives for Chest Disease · 2015
Typereview
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCégep Marie-Victorin
Fundersnot available
KeywordsVareniclineBupropionSmoking cessationMedicineCOPDNicotine replacement therapyPharmacotherapyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Because stopping smoking is such a pressing necessity for COPD smokers physicians should use smoking cessation treatments aggressively. For optimal efficacy smoking cessation in COPD smokers should combine behavioral and pharmacological treatments. Three types of pharmacological treatments are proven to be safe and effective: Nicotine Replacement Therapy (NRT), Bupropion and Varenicline. Use of NRT, bupropion or varenicline, single or in combination, at standard doses or at high doses, for 8-12 weeks or for more than 6-12 months have proven to help these patients to quit. For optimizing efficacy these medications can also be introduced some weeks before actual quitting. In COPD smoking patients that are not interested in stopping completely or abruptly these medications can be used to aid cessation in a more gradual way. Pharmacotherapy to aid cessation in COPD smokers have proven to be highly cost effective.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.180
GPT teacher head0.434
Teacher spread0.253 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations11
Published2015
Admission routes1
Has abstractyes

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