Delayed Reward Discounting as a Prognostic Factor for Smoking Cessation Treatment Outcome: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: While large proportions of smokers attempt to quit, rates of relapse remain high and identification of valid prognostic markers is of high priority. Delayed reward discounting (DRD) is a behavioral economic index of impulsivity that has been associated with smoking cessation, albeit inconsistently. This systematic review sought to synthesize the empirical findings on DRD as a predictor of smoking cessation treatment outcome, to critically appraise the quality of the literature, and to propose directions for future research. AIMS AND METHODS: A total of 734 articles were identified, yielding k = 14 studies that met the eligibility criteria. The Quality in Prognosis Studies (QUIPS) tool was used to assess methodological quality of the included studies. RESULTS: Individual study methods were highly heterogeneous, including substantial variation in research design, DRD task, clinical subpopulation, and treatment format. The predominant finding was that steeper DRD (higher impulsivity) was associated with significantly worse smoking cessation outcomes (10/14 studies). Negative results tended to be in pregnant and adolescent subpopulations. The QUIPS results suggested low risk of bias across studies; 11/14 studies were rated as low risk of bias for 5/6 QUIPS domains. CONCLUSIONS: This review revealed consistent low-bias evidence for impulsive DRD as a negative prognostic predictor of smoking cessation treatment outcome in adults. However, methodological heterogeneity was high, precluding meta-analysis and formal tests of small study bias. The prospects of targeting impulsive DRD as a potentially modifiable risk factor or providing targeted treatment for smokers exhibiting high levels of discounting are discussed. IMPLICATIONS: These findings indicate consistent evidence for DRD as a negative prognostic factor for smoking cessation outcome in adults. As such, DRD may be a useful as a novel treatment target or for identifying high-risk populations requiring more intensive treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".