Bias in hospital samples used for injury research based on consent wording
Bibliographic record
Abstract
Background Many injury studies involve samples of hospitalised or emergency room emergency department patients. In studies involving follow-up, consent is required when patients are first identified. When the proportion who refuse is high, the study sample may be biased in ways that distort the validity of the results. This study aimed to determine if wording requiring active consent resulted in more refusals than wording that was essentially passive that is where consent is implied in the absence of direct refusal. Methods The Canadian Hospital Injury Reporting and Prevention Program (CHIRPP) is based in 14 hospitals, 10 being paediatric. It uses a two-page questionnaire to describe how the injury occurred and what was injured. We chose 5 of the 10 paediatric hospitals; 3 used passive wording and 2 used active wording for consent to follow-up. We employed logistic regression to analyse the effect of wording on consent rates and whether the effect was influenced by patient demographic and clinical characteristics. Results 63% of parents gave consent for follow-up at hospitals using passive consent wording versus 53% where active wording was used. Refusals were highest for 15–19 year olds, families in lower income neighbourhoods, and motor vehicle passenger injuries. After covariate adjustment, passive wording increased likelihood of consent twofold. Conclusion Caution is needed when interpreting results from studies using hospital samples, especially when wording of consent for follow-up requires direct affirmation.
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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 | 0.413 | 0.623 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".