The Nature and Clinical Significance of Preinjury Recall Bias Following Mild Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: Patients with mild traumatic brain injury (MTBI) often underestimate their preinjury symptoms. This study aimed to clarify the mechanism underlying this recall bias and its contribution to MTBI outcome. SETTING: Level I trauma center. PARTICIPANTS: Patients with uncomplicated MTBI (N = 88) and orthopedic injury (N = 67). DESIGN: Prospective longitudinal. MAIN MEASURES: Current and retrospective ratings on the British Columbia Postconcussion Symptom Inventory, completed at 6 weeks and 1 year postinjury. RESULTS: Preinjury symptom reporting was comparable across groups, static across time, and associated with compensation-seeking. High preinjury symptom reporting was related to high postinjury symptom reporting in the orthopedic injury group but less so in the MTBI group, indicating a stronger positive recall bias in highly symptomatic MTBI patients. Low preinjury symptom reporting was not a risk factor for poor MTBI outcome. CONCLUSION: The recall bias was stronger and more likely clinically significant in MTBI patients with high postinjury symptoms. Multiple mechanisms appear to contribute to recall bias after MTBI, including the reattribution of preexisting symptoms to MTBI as well as processes that are not specific to MTBI (eg, related to compensation-seeking).
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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 | 0.015 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".