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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".