MPI Profile Classifications and Associated Clinical Findings Among Litigating Motor Vehicle Collision Patients
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to examine differences in precollision, pericollision, and postcollision clinical variables across litigating motor vehicle collision (MVC) patients who were classified as Dysfunctional (DYS), Interpersonally Distressed (ID), or Adaptive Copers (ACs) based on Multidimensional Pain Inventory (MPI) profile classifications. MATERIALS AND METHODS: A sample of 240 MVC patients who sustained serious physical injuries and experienced MVC-related chronic pain completed the MPI and provided responses to a semistructured psycholegal interview designed to elicit injury-related and pain-related symptoms and treatments, determine the presence and impact of precollision experiences, and render psychiatric diagnoses and ratings of psychological disability. RESULTS: A significant multivariate effect of MPI profile group on postcollision variables was revealed, with the DYS and ID groups reporting more pain sites than the AC group and the DYS group receiving more recommendations for treatment than the AC group. Larger proportions of the DYS and ID groups were diagnosed as experiencing major depressive disorder than the AC group. A rating of total psychological disability was applied most often to members of the ID group, with partial psychological disability applied most often to members of the DYS group, and no psychological disability applied most often to members of the AC group. DISCUSSION: This study extends the MPI literature by establishing the usefulness of the measure in determining those reports of MVC-related pain and emotional distress that are most likely to be associated with postcollision psychological disability. The current study supports the usefulness of MPI profile classifications in identifying MVC patients who are likely to require and benefit from intensive psychological and other rehabilitative interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".