Hot off the press: It's (un)happy hour again—Mortality in younger patients with alcohol‐related ED attendances
Bibliographic record
Abstract
Alcohol is a major cause of mortality and morbidity across the world,1 and ED attendances due to it are rising.2, 3 Adults who attend ED with alcohol-related problems are at an increased risk of death in the following year,4 but the prognostic effect of the increasing numbers of alcohol-related attendances in adolescents and young adults2 has not been specifically addressed. The authors of this study analyzed the 1- and 3-year mortality of adolescents and young adults with a first ED presentation for an alcohol-related issue and causes and predictors of death. This article is a retrospective cohort study; data were routinely collected within the Ontario universal health care system in Canada between 2009 and 2015. The authors included all patients with a least one ED visit who were aged 12–29 years at the time of the visit. They excluded patients not resident in Ontario, those who were not continuously eligible for the Ontario Health Insurance Plan for 2 years before and 3 years after the visit, and those with an alcohol-related attendance or hospitalization in the prior 2 years. The primary outcome was mortality at 1 year. Secondary outcomes were mortality at 3 years, cause of death, and predictors of death. As a study using routinely collected data, the authors are dependent on the quality of the information within the data sets they use. This is likely to be highly accurate in terms of demographics but the use of ICD-10 codes to identify alcohol-related attendances risks underidentifying attendances (such as accidental injury or intimate partner violence) where alcohol use was involved but not the chief complaint. The use of trained coding specialists to extract diagnostic information by this group is also likely to improve quality. In these analyses, authors are restricted to analyzing variables (such as age and gender) that are routinely collected and in the groups in which they are collected. As a result, some variables that might be associated with outcomes (such as alcohol withdrawal symptoms, chaotic lifestyle, neurodiversity, and gender nonconformity) were not available. The authors were only able to explore some issues of interaction between variables; specifically they looked at age and gender. This means that other combinations of variables that may be prognostically important (for example, is the risk of mortality disproportionately higher if the patient is aged 25–29 and poor than if they are the same age and from the highest income bracket) may not have been identified. A total of 71,778 patients had at least one alcohol-related ED visit (of 2,340,097 patients with any ED visit). One-year mortality was 0.35% in the alcohol group versus 0.1% in the nonalcohol group, giving an adjusted hazard ratio of 3.07 (95% confidence interval [CI] 2.69–3.51). This hazard ratio was higher in patients aged 25–29 (5.33, 95% CI 4.38–6.49) but unaffected by gender, neighborhood income quintile, and rurality. The top causes contributing to death were trauma, drugs (opioid and nonopioid), alcohol and self-harm. X poll by @thesgem. Steve Flindall @flindall_steve Unfortunately this is not very surprising to me. Star Bright @StarBrightRain Whoa. Very eye-opening. #paperinapic by @kirstychallen. This cohort study shows that in Ontario a first ED presentation for an alcohol-related issue is associated with higher mortality in adolescents and young adults. The authors declare no conflicts of interest.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".