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Record W2099561661 · doi:10.1183/09031936.00065009

Effects of cannabis on lung function: a population-based cohort study

2009· article· en· W2099561661 on OpenAlexaff
Robert J. Hancox, Richie Poulton, Matthew R. Ely, David Welch, D. Robin Taylor, Christene R. McLachlan, Jillian Greene, Terrie E. Moffitt, Avshalom Caspi, Malcolm R. Sears

Bibliographic record

VenueEuropean Respiratory Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsMcMaster University
FundersNational Institute of Mental Health
KeywordsMedicineCannabisLung functionCohortCohort studyDronabinolPopulationInternal medicineLungPsychiatryEnvironmental healthCannabinoid

Abstract

fetched live from OpenAlex

The effects of cannabis on lung function remain unclear and may be different from those of tobacco. We compared the associations between use of these substances and lung function in a population-based cohort (n = 1,037). Cannabis and tobacco use were reported at ages 18, 21, 26 and 32 yrs. Spirometry, plethysmography and carbon monoxide transfer factor were measured at 32 yrs. Associations between lung function and exposure to each substance were adjusted for exposure to the other substance. Cumulative cannabis use was associated with higher forced vital capacity, total lung capacity, functional residual capacity and residual volume. Cannabis was also associated with higher airway resistance but not with forced expiratory volume in 1 s, forced expiratory ratio or transfer factor. These findings were similar among those who did not smoke tobacco. In contrast, tobacco use was associated with lower forced expiratory volume in 1 s, lower forced expiratory ratio, lower transfer factor and higher static lung volumes, but not with airway resistance. Cannabis appears to have different effects on lung function from those of tobacco. Cannabis use was associated with higher lung volumes, suggesting hyperinflation and increased large-airways resistance, but there was little evidence for airflow obstruction or impairment of gas transfer.

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)0.000
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 score0.124
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.289
Teacher spread0.277 · 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

Citations178
Published2009
Admission routes1
Has abstractyes

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