The Effect of Total Body Water on the Relationship Between Alcohol Consumption and Carbohydrate‐Deficient Transferrin
Bibliographic record
Abstract
BACKGROUND: Clinicians agree that alcoholism commonly is overlooked in their patients, and that treating the symptoms without directing therapy to the underlying cause at best delays an inevitable decline in the patient's general health and well-being. The current analysis focused on carbohydrate-deficient transferrin (CDT), a promising biological marker of dangerous alcohol consumption. METHODS: Included in our study were men (730) and women (613) from study sites in Canada, Brazil, and Japan. All subjects were participants in the WHO/ISBRA Study on State and Trait Markers of Alcoholism, who completed an extensive demographic, medical, and behavioral survey and provided blood samples for determination of CDT levels. ANOVA and chi2 test for equality were used to examine the effect of total body water (TBW) on the alcohol consumption/CDT relationship. To examine whether accounting for differences in TBW improved the diagnostic properties of CDT when used as a state marker for alcohol consumption, odds ratios were calculated for men and women separately. RESULTS: Our results show that accounting for individual differences in TBW significantly influenced the alcohol consumption/CDT dose-response relationship. The effect of TBW was different for men compared with women. When we used a consumption cutoff value of 40 g/day and the CDTect recommended cutoffs (20 for men; 27 for women), adjusting for differences in TBW significantly increased diagnostic performance of CDT in men but not women. CONCLUSIONS: The dependence of CDT measures on body water content needs to be taken into account to maximize the performance of CDT as an effective state marker of alcohol consumption in males.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".