The Role of Blame Attribution in Severity of Prolonged Concussion Recovery
Bibliographic record
Abstract
Psychological risk factors are associated with prolonged recovery from Concussion. We investigated whether blame attribution is another risk factors for prolonged recovery. 91 new patients presenting with prolonged recovery from Concussion at a Canadian subspecialty concussion clinic were included in the study. Participants were separated into three groups: those attributing external blame for their concussion (n=70), those describing the incident as accidental (n=20), and those attributing internal blame (n=1). Observations included: population variables, subjective percent recovered (SPR), duration of symptoms thus far, and presence of symptoms. Participants were more likely to belong to the external blame group versus the accidental or internal blame groups (p<0.0001). Mean SPR in accidental was 73%, compared to 44% in external (p<0.0001). The following occurred more in external versus accidental (relative risk[95% confidence interval]): PTSD (32.24[2.08-500.12]), depression (3.78[1.56-9.19]), anxiety (3[1.53-5.89]), headache (1.21 [0.97-1.51]), irritability (2.76[1.40-5.44]), and cognitive symptoms (1.77[1.13-2.77]). There were no differences in the distribution of sleep disorders and vestibular symptoms. Mean symptom duration thus far was 10.2 months in accidental and 22.5 months in external (p=0.001). External and accidental groups did not differ in age (p=0.938), number of concussions (p=0.72), sex (p=0.908), or pre-existing mental illness (p=0.735). Patients with prolonged recovery are more likely to externally attribute blame for their initiating concussion, which is associated with a longer, more severe course. This study suggests blame attribution could be used to determine patients at risk of prolonged recovery from concussion.
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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 | 0.001 |
| 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".