Functional Recovery Measures for Spinal Cord Injury: An Evidence-Based Review for Clinical Practice and Research
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: The end goal of clinical care and clinical research involving spinal cord injury (SCI) is to improve the overall ability of persons living with SCI to function on a daily basis. Neurologic recovery does not always translate into functional recovery. Thus, sensitive outcome measures designed to assess functional status relevant to SCI are important to develop. METHOD: Evaluation of currently available SCI functional outcome measures by a multinational work group. RESULTS: The 4 measures that fit the prespecified inclusion criteria were the Modified Barthel Index (MBI), the Functional Independence Measure (FIM), the Quadriplegia Index of Function (QIF), and the Spinal Cord Independence Measure (SCIM). The MBI and the QIF were found to have minimal evidence for validity, whereas the FIM and the SCIM were found to be reliable and valid. The MBI has little clinical utility for use in the SCI population. Likewise, the FIM applies mainly when measuring burden of care, which is not necessarily a reflection of functional recovery. The QIF is useful for measuring functional recovery but only in a subpopulation of people with SCI, and substantial validity data are still required. The SCIM is the only functional recovery outcome measure designed specifically for SCI. CONCLUSIONS: The multinational work group recommends that the latest version of the SCIM (SCIM III) continue to be refined and validated and subsequently implemented worldwide as the primary functional recovery outcome measure for SCI. The QIF may continue to be developed and validated for use as a supplemental tool for the nonambulatory tetraplegic population.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".