Alcohol consumption in a non-clinical sample: The role of sweet-liking, PROP bitterness and sex
Bibliographic record
Abstract
Some previous studies have suggested an association between sweet-liking and alcohol use in male alcohol-dependent individuals. However, if sweet-liking is to have value as an indicator of potentially hazardous drinking behaviour, the relationship needs to be established in non-dependent individuals, and determined for women and younger individuals, who may be at increased risk of alcohol use disorders. This study comprised of a non-clinical sample of 223 male and female university students. Responsiveness to 3 sucrose-impregnated taste discs (9 g/l, LSD; 90 g/l, MSD; 900 g/l, HSD) and a 50 mM 6-n-propyl-2-thiouracil (PROP)-impregnated disc were collected and used to classify participants as sweet-likers (HSD/LSD ≥ 1.5), sweet-dislikers (HSD/LSD < 1.5), PROP non-tasters (gLMS intensity score ≤ 12 mm), PROP medium-tasters (13-55 mm), or PROP supertasters (≥ 56 mm). Data on familial history of alcoholism, alcohol intake, and hazardous drinking (Alcohol Use Disorders Identification Test, AUDIT) were also collected. Two-way Analysis of Variance showed a significant main effect for sweet-liking on alcohol consumption in males (F (1)=4.10, p=0.04), with monthly intake (natural log transformed) of sweet-liking males higher than sweet-disliking males. Neither alcohol consumption (t (191)=1.97, p=0.23), sweet-liking (ratio of HSD liking over LSD; t (191)=1.97, p=0.41), or PROP responsiveness (t(191)=1.97, p=0.56) varied with AUDIT classification or family history of alcoholism (p> 0.05). Overall, our results partially support the hypothesis that ethanol and sucrose influence the opioid reward system in the brain in a similar way to reinforce use.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".