Rasch Analysis and Functional Measurement in Post-Hospital Brain Injury Rehabilitation
Bibliographic record
Abstract
Rasch analysis is a statistical technique used in determining statistical properties of functional measures for use in research and treatment. The technique was used in the current study to determine the reliability and validity of the Mayo Portland Adaptability Inventory-Version 4 (MPAI-4) for use with three different acquired brain injury samples. Subjects were 777 adults (each group comprised of 259 individuals) with acquired brain injury treated in one of three rehabilitation program types: Neurorehabilitation (NR), Neurobehavioral (NB), or Supported Living (SL). The MPAI-4 was administered to each participant upon admission to program. Rasch analysis was conducted to assess item fit, reliability, and separation statistics for MPAI-4 assessments conducted within each program. Item difficulty values were examined to determine if the MPAI-4 differentiated among groups based on deficit profiles. The results revealed that for each group, fit statistics fell with appropriate levels (0.5 – 1.5) for at least 24 of 29 items. Rasch person reliability statistics were 0.89 for NR and NB, and 0.90 for SL. Item reliability was 0.99 for each of the groups. Item difficulty values accurately differentiated the three groups based on their specific deficit profiles expected. Specifically, NR participants’ greatest deficits demonstrated by the MPAI-4 were within cognitive and physical functions. For the NB participants, the greater deficits demonstrated were within the behavioral and adjustment items. Supported Living participants had the most limitation within the instrumental activities of daily living items. As in prior research findings, the current Rasch analysis supported the use of the MPAI-4 within this heterogeneous, acquired brain injury population. This unique statistical approach translates to treatment priorities that may assist clinicians with identifying treatment goals specific to unique treatment group characteristics (e.g., NR, NB, and SL).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".