Development and Validation of Crosswalks Between FIM® and SCIM III for Voluntary Musculoskeletal Movement Functions
Bibliographic record
Abstract
Background. In spinal cord injury, there are multiple databases containing information on functional recovery, but data cannot be pooled or compared due to differences in how function is measured. A crosswalk is needed to link or convert scores between instruments. Objectives. To create a crosswalk between the voluntary musculoskeletal movement items in the Functional Independence Measure (FIM®) and the Spinal Cord Independence Measure III (SCIM III) for spinal cord injury. Methods. Retrospective datasets with FIM® and SCIM III on the same people were used to develop (Swiss dataset, n = 662) and validate (US, n = 119, and Canadian datasets, n = 133) the crosswalks. Three different crosswalk methods (expert panel, equipercentile, and Rasch analysis) were employed. We used the correlation between observed scores on FIM® and SCIM III to crosswalked scores as the primary criterion to assess the strength of the crosswalk. Secondary criteria such as score distributions, Cohen’s effect size, point differences, and subgroup invariance were also evaluated. Results. All three methods resulted in strong correlation coefficients, exceeding the primary criterion value of r = .866 (.897–.972). Assessment of secondary criteria suggests the equipercentile and Rasch methods produced the strongest crosswalks. Conclusions. The Rasch FIM®/SCIM III crosswalk is recommended because it is based on co-calibration of linearized measures, allowing for more sophisticated parametric analyses. The crosswalk will allow comparisons of voluntary musculoskeletal functional recovery across international databases using different functional measures, as well as different systems of care and rehabilitation approaches.
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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 | 0.068 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".