Perceived Change in Physical, Cognitive, and Emotional Symptoms after Mild Traumatic Brain Injury in Patients with Pre-Injury Anxiety or Depression
Bibliographic record
Abstract
The objective of this study was to compare patients with acute-to-subacute mild traumatic brain injury (mTBI) on post-concussion symptom reporting based on whether they retrospectively recalled experiencing pre-injury anxiety or depression. Patients with mTBI ( n = 297; 40.4% men; mean = 38.2 years old, standard deviation [SD] = 14.0, range = 17–65), referred from an emergency department in Taipei, Taiwan, were seen in a neurosurgical outpatient clinic on average 7.7 days since injury (SD = 5.7, range = 0 – 21 days), at which time they completed a checklist of post-concussion symptoms. Patients rated their current symptom severity and retrospectively rated their pre-injury symptom severity on 15 physical, cognitive, and emotional symptoms. Patients were grouped based on whether they did or did not have mild or greater pre-injury anxiety or depression based on this scale. Those with pre-injury anxiety or depression had greater pre-injury (all p' s < 0.001, d range: 0.92–2.03) and post-injury (all p' s < 0.001, d range: 0.65–1.00) symptom severity. However, when analyzing perceived change in symptoms (i.e., post-injury ratings minus pre-injury ratings), only perceived change in cognitive symptoms differed across groups ( p = 0.018, d = 0.29), which became non-significant after controlling for gender. Greater post-concussion symptom severity in patients with pre-existing mental health problems may be mostly attributable to elevated symptoms before injury. These findings demonstrate the clinical value of retrospective pre-injury symptom assessment in mTBI management. Greater post-concussion symptom severity in patients with pre-injury mental health problems may represent a continuation of greater pre-injury symptom severity rather than a greater increase in symptom severity after mTBI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".