How Severe should Symptoms be before we say Someone is Suffering from Postconcussion Syndrome?
Objective: This study aimed to examine the diagnosis of postconcussive symptoms among a group of people with subjective postconcussive symptoms (PCS) complaints using a self-reported checklist.
Method: A cross-sectional design was adopted with a total of 102 patients with PCS and 141 normal controls. The Rivermead Postconcussion Symptoms Checklist was administered to all participants. Analysis using the Rasch Model was undertaken.
Results: Calibration of the 16 items representing the postconcussive symptom variable found all items to have mean square infit and outfit statistics within a range of 1.03 to 1.07. Person separation and item separation were 2.94 and 6.02 logits units respectively, indicating the scale spread both the person scores and item scores out over a large portion of the logits continuum. The common matrix of symptom profiles indicated significant effects of gender (p<0.005) and litigation (p<0.0004). The patient group reported significantly higher symptoms than the healthy group (p<0.0001) with differential symptom endorsement along the logits unit continuum.
Conclusion: The application of Rasch model analysis successfully set up a common matrix to discriminate patients with postconcussive symptoms from healthy people along the logits unit continuum.