Rasch Analysis of the Postconcussive Symptom Questionnaire: Measuring the Core Construct of Brain Injury Symptomatology
Bibliographic record
Abstract
The Postconcussive Symptom Questionnaire (PCSQ; Lees-Haley, 1992 Lees-Haley, PR. 1992. Neuropsychological complaint base rates of personal injury claimants. Forensic Reports, 5(4): 385–391. [Google Scholar]) is purported to measure four constructs. These include psychological, cognitive, somatic, and infrequency (i.e., items intended to reflect negative impression management) symptoms. The utility and validity of Postconcussive Syndrome (PCS) as a diagnostic condition continues to be debated. To this end, examining the instruments used to measure postconcussive symptoms can increase our understanding with respect to this issue. The aim of this study was to derive a revised PCSQ to target the core construct of subjective symptoms reported by persons with traumatic brain injury (TBI). A total of 133 people with mild to severe TBI completed the 45-item PCSQ. Items were scored dichotomously, as symptom present or absent. Rasch analysis, based on the mathematical model formulated by Rasch (1960 Rasch, G. 1960. Probabilistic models for some intelligence and attainment tests, Copenhagen, , Denmark: Danmarks paedagogiske Institut. [Google Scholar]), was used to derive the revised PCSQ. Misfitting and redundant items were removed and a second model containing 19 items was fitted. The revised PCSQ-19 had superior psychometric qualities; reliability was 0.81. The PCSQ-19 provides a more targeted, unidimensional assessment of subjective symptoms following brain injury. The findings also revealed information related to symptom hierarchy which can further our understanding of PCS.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".