The Effect of Injury Diagnosis on Illness Perceptions and Expected Postconcussion Syndrome and Posttraumatic Stress Disorder Symptoms
Bibliographic record
Abstract
OBJECTIVE: To determine if systematic variation of diagnostic terminology (ie, concussion, minor head injury [MHI], mild traumatic brain injury [mTBI]) following a standardized injury description produced different expected symptoms and illness perceptions. We hypothesized that worse outcomes would be expected of mTBI, compared with other diagnoses, and that MHI would be perceived as worse than concussion. METHOD: 108 volunteers were randomly allocated to conditions in which they read a vignette describing a motor vehicle accident-related mTBI followed by a diagnosis of mTBI (n = 27), MHI (n = 24), concussion (n = 31), or, no diagnosis (n = 26). All groups rated (a) event "undesirability," (b) illness perception, and (c) expected postconcussion syndrome (PCS) and posttraumatic stress disorder (PTSD) symptoms 6 months after injury. RESULTS: There was a statistically significant group effect on undesirability (mTBI > concussion and MHI), PTSD symptomatology (mTBI and no diagnosis > concussion), and negative illness perception (mTBI and no diagnosis > concussion). CONCLUSION: In general, diagnostic terminology did not affect anticipated PCS symptoms 6 months after injury, but other outcomes were affected. Given that these diagnostic terms are used interchangeably, this study suggests that changing terminology can influence known contributors to poor mTBI outcome.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".