Recall bias across 7 days in self‐reported alcohol consumption prior to injury among emergency department patients
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Recall bias is a concern in self-reported alcohol consumption, potentially accounting for varying risk estimates for injury in emergency department (ED) studies. The likelihood of reporting drinking for the same 6-h period each day of the week for a full week preceding the injury event is analysed among injured ED patients. DESIGN AND METHODS: Probability samples of patients 18 years old and older were interviewed in two ED sites in Vancouver and one in Victoria, BC (n = 1191). Generalized estimating equation modelling was used to predict the likelihood of reporting drinking for the same 6-h period prior to the injury event for each day of the week, compared to day 7 as the reference recall day, for a full week preceding the event. Recall by frequency of drinking and frequency of heavy drinking was analysed. RESULTS: Drinking was significantly more likely to be reported for each of the first 3 days of recall compared to 7-day recall and highest for 1-day recall (odds ration 1.55; = 0.002). Patients who reported ≥ weekly drinking and 5+ drinking < monthly were significantly more likely to report drinking for each of the first 3 days of recall (compared to 7-day recall). DISCUSSION: Findings suggest the first 3 days prior to injury may be a less biased multiple-matched control period than longer periods of recall in case-crossover studies. CONCLUSION: Length of accurate recall may be important to consider in case-crossover analysis and other study designs that rely on patient self-report such as the Timeline Followback. [Cherpitel CJ, Ye Y, Stockwell T, Vallance K, Chow C. Recall bias across 7 days in self-reported alcohol consumption prior to injury among emergency department 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 | 0.026 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".