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Record W2894510151 · doi:10.2105/ajph.2018.304649

Evaluating the Evidence on Sitting, Smoking, and Health: Is Sitting Really the New Smoking?

2018· review· en· W2894510151 on OpenAlexaff
Jeff K. Vallance, Paul A. Gardiner, Brigid M. Lynch, Adrijana D’Silva, Terry Boyle, Lorian Taylor, Steven T. Johnson, Matthew P. Buman, Neville Owen

Bibliographic record

VenueAmerican Journal of Public Health · 2018
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsAthabasca University
Fundersnot available
KeywordsSittingMedicineConfidence intervalHazard ratioRelative riskDemographyPublic healthEpidemiologyConfusionEnvironmental healthPsychologyInternal medicine

Abstract

fetched live from OpenAlex

Sitting has frequently been equated with smoking, with some sources even suggesting that smoking is safer than sitting. This commentary highlights how sitting and smoking are not comparable. The most recent meta-analysis of sedentary behavior and health outcomes reported a hazard ratio of 1.22 (95% confidence interval [CI] = 1.09, 1.41) for all-cause mortality. The relative risk (RR) of death from all causes among current smokers, compared with those who have never smoked, is 2.80 (95% CI = 2.72, 2.88) for men and 2.76 for women (95% CI = 2.69, 2.84). The risk is substantially higher for heavy smokers (> 40 cigarettes per day: RR = 4.08 [95% CI = 3.68, 4.52] for men, and 4.41 [95% CI = 3.70, 5.25] for women). These estimates correspond to absolute risk differences of more than 2000 excess deaths from any cause per 100 000 persons per year among the heaviest smokers compared with never smokers, versus 190 excess deaths per 100 000 persons per year when comparing people with the highest volume of sitting with the lowest. Conflicting or distorted information about health risks related to behavioral choices and environmental exposures can lead to confusion and public doubt with respect to health recommendations.

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)0.009
metaresearch head score (Gemma)0.043
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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.302
GPT teacher head0.497
Teacher spread0.195 · 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

Citations67
Published2018
Admission routes1
Has abstractyes

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