A feasibility study of digital self-report measurement for brain injury patients utilizing an adapted version of the Mayo-Portland Adaptability Inventory – fourth edition
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine the clinical relevance and usability of the digital self-report version of the Mayo-Portland Adaptability Inventory - fourth edition, MPAI-4 (MPAI-4-S-dig). In its paper version, MPAI-4 is well validated for patients with acquired brain injuries (ABIs) and neurological disorders (NDs), but time consuming. An additional aim was to investigate whether MPAI-4-S-dig is reliable for repeated measurements. SETTING: Community neurorehabilitation in Stockholm, Sweden. METHODS: MPAI-4-S-dig was administered to 40 patients with ABI or ND 2 weeks apart. Test-retest reliability was assessed using the intraclass correlation coefficient (ICC); clinical relevance of data was assessed through Pearson's Correlation Coefficient with Montreal Cognitive Assessment (MoCA), the Community Integration Questionnaire - Revised (CIQ-R), and Hospital Anxiety and Depression Scale (HADS). RESULTS: ICC values ranged from 0.86 to 0.93 for total and subscales. Significant correlations were found between MPAI-4-S-dig participation and CIQ-R Total, social integration and home integration and MoCA naming, MPAI-4-S-dig adjustment and CIQ-R Social integration, MPAI-4-S-dig Total and all subscale scores and HADS Anxiety score, MPAI-4-S-dig Total, abilities and participation and HADS Depression. CONCLUSION: The demonstrated reliability and clinical relevance of MPAI-4-S-dig for patients undergoing neurorehabilitation permits the implementation of digital data capture in patients with mild acquired cognitive impairment.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".