A systematic review and analysis of data reduction techniques for the CReSS smoking topography device
Bibliographic record
Abstract
Introduction : Characterising smoking behaviour in an objective and ecologically valid manner is integral to understanding health complications associated with tobacco use. Smoking topography (ST) provides a representation of the physical attributes of smoking. However, there is no clear guidance on ST data exclusion and reduction techniques and the impact of different techniques. Methods : A search was conducted using MEDLINE, PubMed, and Scopus and limited to studies published between 2001‒2012. The search identified 23 studies using the CReSS device. Results : Few studies reported data reduction ( n = 9) and exclusion ( n = 4) criteria. Four data reduction techniques emerged and were applied to an existing dataset ( n = 193, M age = 38.98, FTND = 5.19, mean 17.23 cigarettes/day). Using repeated measures ANOVA, there were significant ( p < 0.05) differences among all techniques for puff volume, peak flow, puff duration and interpuff interval, which were attenuated upon controlling for puff count. Conclusions : This review highlights the inconsistency in the literature regarding the disclosure of smoking topography data treatment and provides clear evidence that outcomes vary depending on the technique used. Greater transparency is needed and consideration should be given by researchers to the potential impact of methodological decisions on study findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".