A Synthesis of Best Evidence for the Restoration of Upper-Extremity Function in People with Tetraplegia
Bibliographic record
Abstract
PURPOSE: Because upper-limb function represents overall function for individuals with tetraplegia, the restoration of upper-extremity function is exceedingly important for this population. The purpose of this review was to identify interventions that optimize upper-limb function after tetraplegia based on best available evidence. METHODS: A search of MEDLINE, AMED, and PubMed with the search terms "hand function AND tetraplegia" and "upper limb function AND tetraplegia" found 384 articles. After elimination of duplicates and review of titles and abstracts, 43 studies were found to be applicable. Study quality of all applicable studies was assessed with a modified version of the Scottish Intercollegiate Guidelines Network for Cohort Studies methodology. RESULTS: The applicable studies were organized into three categories: conventional therapies (CT), electrical stimulation therapies (ES), and surgical interventions (SI). The proportion of papers in each category that presented with sufficient methodological quality to contribute to best evidence was as follows: CT: 0/2; ES: 10/21; SI: 6/20. CONCLUSIONS: ES therapies are beneficial as assistive technologies and as therapeutic intervention in the subacute phase of recovery. SIs are suitable for individuals who meet very specific criteria for tendon-transfer surgery. Further clinical trials are warranted for ES and SI therapies to substantiate prescription of therapeutics.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".