Alcohol Consumption in Patients with Psoriasis and its Relationship to Disease Severity
Bibliographic record
Abstract
Moderate to severe psoriasis is associated with increased alcohol intake and excessive mortality from alcohol-related causes. Alcohol biomarkers are those providing an objective measurement of its consumption. The objective of this study is to assess alcohol consumption in a cohort of patients with psoriasis and to investigate the influence of alcohol intake on disease severity. Patients with psoriasis hospitalized at the Clinic of Dermatology, in Mother Teresa University Hospital were recruited in this cross- sectional study. Alcohol consumption was assessed via CAGE questionnaire and the self- reported amount of alcohol consumed, whereas disease severity was evaluated via PASI scoring system. Blood specimens were taken at admission and levels of gamma glutamyltransferase (GGT), alanine aminotransferase (ALT), aspartate aminotransferase (AST) and mean corpuscular volume of erythrocytes (MCV) were measured. Statistical analysis was performed using SPSS 20.0 statistical package. A total of 62 in- patients completed the study. Significant correlations were observed between GGT and AST values with raki and beer consumption, ALT value with raki, beer and wine consumption and MCV value with raki consumption. Disease severity did not correlate significantly with raki and beer consumption (p> 0.05). Logistic regression analysis between Psoriasis Area and Severity Index (PASI) score and raki consumption in male patients with psoriasis duration of more than 3 years resulted in statistical significance (b= 23.5, p< 0.05). Combination of parameters related to chronic alcohol consumption offers advantage over every isolated test. Measurement of simple laboratory parameters combined with self- report methods of consumption allows identification of users.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".