“Good Old Days” Bias Following Mild Traumatic Brain Injury
Bibliographic record
Abstract
A small percentage of people with a mild traumatic brain injury (MTBI) report persistent symptoms and problems many months or even years following injury. Preliminary research suggests that people who sustain an injury often underestimate past problems (i.e., "good old days" bias), which can impact their perceived level of current problems and recovery. The purpose of this study was to examine the influence of the good old bays bias on symptom reporting following MTBI. The MTBI sample consisted of 90 referrals to a concussion clinic (mean time from injury to evaluation = 2.1 months, SD = 1.5, range = 0.8-8.1). All were considered temporarily fully disabled from an MTBI and they were receiving financial compensation through the Worker's Compensation system. Patients provided post-injury and pre-injury retrospective ratings on the 16-item British Columbia Post-concussion Symptom Inventory (BC-PSI). Ratings were compared to 177 healthy controls recruited from the community and a local university. Consistent with the good old bays bias, MTBI patients retrospectively endorsed the presence of fewer pre-injury symptoms compared to the control group. Individuals who failed effort testing tended to retrospectively report fewer symptoms pre-injury compared to those patients who passed effort testing. Many MTBI patients report their pre-injury functioning as better than the average person. This can negatively impact their perception of current problems, recovery from injury, and return to work.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".