Abstract P231: Alcohol Consumption and Hospitalization Risk: Prospective Results From the Moli-sani Study
Bibliographic record
Abstract
Introduction: To evaluate the broad impact of alcohol on health and healthcare utilization, the dose-response relationship of alcohol intake with all-cause and cause-specific hospitalizations was examined. Methods: In the Moli-sani study, an Italian population-based cohort, we followed 20,682 initially healthy individuals (48% men, age ≥35 y) free of CVD or cancer. Alcohol intake in the year before enrolment was assessed by the Italian EPIC-FFQ and classified as: abstainers (referent), ex-drinkers, occasional drinkers (<1 gr/day), 1-12, 12.1-24, 24.1-48 and >48 gr/day. We identified hospitalizations by linkage to the regional hospitalization registry. Cause-specific hospitalizations were assigned by the ICD9 code of the primary admission diagnosis. We estimated incidence rate ratios (IRR) for admission by Poisson regression, accounting for repeated hospitalizations. Results: At baseline, 27% of participants were abstainers, 3% ex-drinkers, 6% occasional drinkers and 64% regular current drinkers. During a median follow-up of 6.3 y, 12,996 hospital admissions occurred. In multivariable analyses, occasional consumption and intake up to 48 gr/day were associated with a lower risk of all-cause hospitalization than was abstention. There was a roughly dose-dependent association with lower risk of hospitalization for vascular disease. Excessive alcohol consumption was associated with a higher risk of hospitalization for alcohol-related diseases (IRR: 1.92, 95% CI: 1.43-2.59) and for cancer (IRR: 1.33, 95% CI: 1.08-1.63). Former drinkers were at higher risk for vascular and alcohol-related hospitalization. No association was observed with hospitalization for trauma. Conclusions: Heavy alcohol consumption is associated with higher risk of hospitalization for alcohol-attributable conditions and cancer, but intake up to 48 gr/day is associated with lower risk of all-cause and vascular hospitalization. These estimates highlight the different healthcare burden imposed by varying levels of alcohol intake.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".