PAPER 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 cover-age, this issue can be examined using population based, administrative databases to obtain information about a cohort that was approached for study enrolment, regardless of whether they ulti-mately agreed to participate. Methods: A sample of 626 consecutive patients with mild TBI was invited to enrol 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
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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 | 0.291 | 0.441 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".