Who is asked about alcohol consumption? A retrospective cohort study using a national repository of Electronic Medical Records
Bibliographic record
Abstract
Documentation of alcohol use in electronic medical record (EMR) informs interventions to reduce alcohol-related morbidity and mortality. This retrospective cohort study explored EMR data from 960 primary care providers participating in the Canadian Primary Care Sentinel Surveillance Network to describe documentation of alcohol use (e.g. none, current or past use) in the EMR. Included providers represented 700,620 adult patients from across Canada with an encounter between 2015 and 2018. Bivariate comparisons characterized the patients with, and without, documentation of alcohol use. Multivariate generalized estimating equation models with logit function assessed patient and provider characteristics associated with (1) documentation of alcohol and (2) patients with heightened risk for alcohol-related problems. Forty percent of patients had alcohol use documentation in the EMR. Light alcohol consumption was recorded for 43.6% of these patients. Male patients (OR1.09, CI 1.07-1.12), who were older (OR1.26, CI 1.23-1.30), had more frequent visits to their provider (OR1.11, CI 1.09-1.13) and had hypertension (OR1.07, CI 1.06-1.09) or depression (OR1.07, CI 1.09-1.14) had higher odds of alcohol documentation. There were 4.7% of patients with a record indicating heightened risk for alcohol-related problems. Male patients (OR3.27 CI 3.14-3.4), patients with depression (OR2.01 CI1.93-2.1) and rural residency (OR1.35 CI1.29-1.42) was associated with risk for alcohol-related problems. Heavy alcohol consumption is associated with an increased risk of negative health outcomes, particularly for patients with certain chronic conditions. However, these patients do not have alcohol use consistently documented in the EMR. Strategies should be designed and implemented to support more consistent alcohol-screening among high-risk patients.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".