MétaCan
Menu
Back to cohort

Smoking kills, quitting heals: the importance of smoking cessation in COPD

2011· letter· en· W1595524942 on OpenAlexaff
Hye Yun Park, Don D. Sin

Bibliographic record

VenueThe Clinical Respiratory Journal · 2011
Typeletter
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of British ColumbiaSt. Paul's Hospital
Fundersnot available
KeywordsMedicineCOPDSmoking cessationChinaLung cancerConsumption (sociology)DiseaseEnvironmental healthInternal medicinePathology

Abstract

fetched live from OpenAlex

We are winning the battle against tobacco in the Western world. Whereas in the early 1960s one in two adults smoked, today fewer than one in five adults are smokers in most industrialised nations 1. We are, however, losing the global war against tobacco because the tobacco epidemic has moved from the West to the East. Since 1960, there has been near tripling in the worldwide consumption of cigarettes, from approximately 2 trillion ‘sticks’ per year to more than 5.7 trillion cigarettes. Every minute, more than 12 million cigarettes are being smoked throughout the world. Regrettably, the worldwide consumption will continue to rise and exceed 6.5 trillion cigarettes by the year 2020, driven largely by China and other emerging nations 2. Nearly 30% of these smokers will develop chronic obstructive pulmonary disease during the course of their lives 3. Of these patients, approximately a third will die from cancer (mostly lung cancer), a third will die from cardiovascular disease (CVD) and the remaining third will die from other causes, most notably respiratory failure and pneumonia 4. This year alone nearly 5 million people will die globally from cigarette-related complications 5. In this issue of the Clinical Respiratory Journal, Godtfredsen and Prescott provide new hope for the 1.2 billion smokers in the world 6. In this comprehensive review, they clearly show that smoking is a modifiable risk factor for morbidity and mortality, and that by quitting early, lives can be saved. There are some important messages in this review that should be highlighted. First, smoking cessation is of proven benefit at any age, but the benefits are greatest for those who stop smoking before the age of 35 years. Second, the cardiovascular system is particularly sensitive to the effects of cigarette smoking. While smoking more than doubles the risk of CVD, quitting smoking reduces the risk by 50% within 1 year of cessation, and by 10–15 years of cessation, the rate of CVD is similar to that of never-smokers. Third, the effects of smoking cessation on the respiratory system are complex. There is clear evidence from the Lung Health Study that smoking cessation modifies the decline in lung function. However, the data on respiratory exacerbations, hospitalisations and mortality are mixed with some demonstrating benefit, while others showing no benefit on these end points. Fourth, although not covered in this review, there is compelling evidence to indicate that smoking cessation reduces lung cancer rates. However, dissimilar to the beneficial effects on the cardiovascular system, the risk of lung cancer in ex-smokers remain persistently elevated, even decades following smoking cessation 4. Overall, quitting smoking reduces total mortality by 42% compared with continued smoking 4. While most clinicians and smokers understand the health benefits of smoking cessation, many continue to smoke. Indeed, In the Western world, nearly three out of four workers intend to quit smoking every year, yet fewer than 5% are able to quit successfully on their own 7, 8. Why is this? The simple answer is that tobacco and more specifically nicotine is highly addictive. When inhaled, nicotine quickly reaches the brain where it binds to nicotinic cholinergic receptors in the ventral tegmental area of the midbrain. This in turn releases dopamine in the nucleus accumbens, inducing ‘pleasure’. Continued smoking over time leads to neuroadaptation and desensitisation of the nicotinic receptors, resulting in tolerance and dependence. Once this occurs, within hours of smoking cessation, smokers develop withdrawal symptoms and craving for cigarettes. This compels many smokers to take up smoking again, especially during times of emotional stress. Thus, for most smokers, ‘will power’ alone cannot effect smoking cessation. Health professionals can, however, provide a pivotal role in helping smokers to achieve permanent cessation. The US Public Health Services recommends that health-care providers use the ‘five As’ to foster smoking cessation: ‘ask’, ‘advise’, ‘assess’, ‘assist’ and ‘arrange’ follow-up 9. First, health professionals should ask all of their patients whether they smoke. If the response is ‘yes’, then the health-care provider should motivate the smoker to quit by advising them of the health benefits of smoking cessation. This should be followed by assessing whether the smoker is interested and committed to smoking cessation. If the smoker is motivated, the health-care provider should assist the smoker to quit by offering behavioural support and pharmacotherapy, if necessary. The most effective pharmacologic agent is varenicline, although it has been associated with nausea, abnormal dreams and even suicidal ideations 10. Alternatives include bupropion and nicotine replacement therapies 8. Finally, close follow-up should be arranged to monitor the progress of the smokers. With this approach, over 20% of smokers will achieve long-term smoking cessation 4. Smoking is a menace worldwide. Although in the Western world, the rates of smoking are decreasing, the rates of smoking are increasing in developing nations 2. As lucidly outlined by Godtfredsen and Prescott, the health benefits of smoking cessation are clear. Health-care professionals have a moral responsibility to identify smokers and assist them in overcoming their addiction to nicotine. By doing so, we can reduce the 5 million smokers who die needlessly every year from their tobacco addiction. The message is unequivocal: smoking kills, but quitting heals.

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)0.014
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.290
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.013
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 head0.135
GPT teacher head0.410
Teacher spread0.275 · 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.

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

Citations6
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueThe Clinical Respiratory JournalSame topicChronic Obstructive Pulmonary Disease (COPD) ResearchFrench-language works237,207