Evaluating recall bias in a case‐crossover design estimating risk of injury related to alcohol: Data from six countries
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Prior work suggests that recall bias may be a threat to the validity of relative risk estimation of injury due to alcohol consumption, when the case-crossover method is used based on drinking during the same six hours period the week prior to injury as the control period. This work explores the issue of alcohol recall bias used in the case-crossover design. DESIGN AND METHODS: Data were collected on injury patients from emergency room studies across six countries (Dominican Republic, Guatemala, Guyana, Nicaragua, Panama and Canada), conducted in 2009-2011, each with n ≈ 500 except Canada (n = 249). Recall bias was evaluated comparing drinking during two control periods: the same six hours period the day before versus the week before injury. RESULTS: A greater likelihood of drinking yesterday compared with last week was seen using data from the Dominican Republic, while lower likelihood of drinking yesterday was found in Guatemala and Nicaragua. When the data from all six countries were combined, no differential drinking between the two control periods was observed. DISCUSSION AND CONCLUSIONS: These findings are in contrast to earlier studies showing a downward recall bias of drinking, and suggest that it may be premature to dismiss the last week case-crossover method as a valid approach to estimating risk of injury related to drinking. However, the heterogeneity across countries suggests that there may be some unexplained measurement error beyond random sampling error.
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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 | 0.168 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".