Outcome after mild traumatic brain injury: an examination of recruitment bias
Bibliographic record
Abstract
OBJECTIVES: Research concerning the natural history after mild traumatic brain injury (TBI) faces a number of methodological challenges, including those related to subject recruitment. The aim of this study was to determine whether subjects who agree to participate in longitudinal research differ from those who do not. The presence of identifiable, selective factors operating during recruitment may be an important source of systematic bias. In Canada, given the presence of universal healthcare coverage, this issue can be examined using population based, administrative databases to obtain information about a cohort that was approached for study enrollment, regardless of whether they ultimately agreed to participate. METHODS: A sample of 626 consecutive patients with mild TBI was invited to enroll in TBI outcome research. Those who agreed to participate (n=272) were compared with those who refused (n=354) on demographic, past health, and injury related variables. Thereafter, using encrypted health card data, the two groups were contrasted with respect to pre-injury and post-injury healthcare utilisation. RESULTS: No premorbid differences between the groups emerged. However, all early indices of TBI severity were significantly worse for the participants group (p<0.001). Consistent with these findings, healthcare utilisation rates were no different before injury, but were significantly increased after injury for the participants (p<0.001), even beyond the period of study enrollment (p<0.001). Differences remained even after controlling for those with significant non-TBI injuries. CONCLUSIONS: Premorbid factors did not predict whether patients comply with, or refuse study participation. However, the participants group was biased toward those with more significant injuries, which translated into higher rates of healthcare utilisation after injury. These results strike a cautionary note, given the apparent systematic bias influencing enrollment in longitudinal studies of mild TBI.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".