Evaluating the Sick Quitting Hypothesis for Frailty Status and Reducing Alcohol Use Among People With HIV in a Longitudinal Clinical Cohort Study
Bibliographic record
Abstract
“Sick quitting”, a phenomenon describing reductions in alcohol consumption following poor health, may explain observations that alcohol appears protective for frailty risk. We examined associations between frailty and reductions in drinking frequency among people with HIV (PWH). At six Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) sites between January 2012 and August 2021, we assessed whether frailty, measured via validated modified frailty phenotype, precedes reductions in drinking frequency. We associated time-updated frailty with quitting and reducing frequency of any drinking and heavy episodic drinking (HED), adjusted for demographic and clinical characteristics in Cox models. Among 5,654 PWH reporting drinking, 60% reported >monthly drinking and 18% reported ≥monthly HED. Over an average of 5.4 years, frail PWH had greater probabilities of quitting (HR:1.56, 95%CI:1.13–2.15) and reducing (HR:1.35, 95%CI:1.13–1.62) drinking frequency, as well as reducing HED frequency (HR:1.58, 95%CI:1.20–2.09) vs. robust PWH. Sick quitting likely confounds the association between alcohol use and frailty risk, requiring investigation for control.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".