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Record W2990242844 · doi:10.1007/s00520-019-05175-4

Awareness of the Harms of Continued Smoking Among Cancer Survivors

2019· article· en· W2990242844 on OpenAlexafffund
Lawson Eng, Devon Alton, Yuyao Song, Jie Su, Qihuang Zhang, Jiahua Che, Delaram Farzanfar, Rahul Mohan, Olivia Krys, Katie Mattina, Christopher R. Harper, Sophia Liu, Tom Yoannidis, Robin Milne, Nazek Abdelmutti, M. Catherine Brown, Ashlee Vennettilli, Andrew Hope, Doris Howell, Jennifer M. Jones, Peter Selby, William K. Evans, Wei Xu, David P. Goldstein, Meredith Giuliani, Geoffrey Liu

Bibliographic record

VenueSupportive Care in Cancer · 2019
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsPublic Health OntarioMcMaster UniversityOntario Institute for Cancer ResearchCentre for Addiction and Mental HealthPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
FundersPosluns Family Foundation
KeywordsMedicineLung cancerSmoking cessationCancerSurvivorship curveQuality of life (healthcare)Logistic regressionCancer survivorshipTobacco smokeInternal medicineEnvironmental healthNursingPathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.339
Teacher spread0.315 · 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 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

Citations18
Published2019
Admission routes2
Has abstractno

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