Who Gets Recruited in Mild Traumatic Brain Injury Research?
Bibliographic record
Abstract
Selection bias, common in traumatic brain injury research, limits the clinical usefulness and generalizability of study findings. The purpose of this study was to examine the effect of different inclusion and exclusion criteria on patient enrollment, and the implications for generalizability, in a mild traumatic brain injury (MTBI) study. The study was conducted at the emergency department (ED) of Tampere University Hospital. Our aim was to study outcome from MTBI in patients who do not have pre-existing conditions or other confounding factors. For this, all consecutive patients with acute head trauma (n=1344) were screened. The study design included three inclusion criteria and nine exclusion criteria. The World Health Organization Collaborating Center for Neurotrauma Task Force criteria for MTBI were used. Of all patients screened, 934 (69.5%) fulfilled the MTBI criteria. For those fulfilling the MTBI criteria, various inclusion and exclusion criteria were applied in order to yield those eligible for the outcome study. Applying these criteria excluded 95.1% of MTBI patients, leaving only 46 patients in the final sample. The final sample and the excluded patients with MTBI significantly differed in age, mechanism of injury, and injury severity characteristics. Many studies recruit fundamentally biased samples that are not generalizable to the population of persons who sustain an MTBI. Studying carefully selected samples is often necessary to address specific research questions, but such studies have serious limitations in terms of translating research findings into clinical practice.
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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 | 0.338 | 0.522 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".