3-Dimensional Printing in Rehabilitation: Feasibility of Printing an Upper Extremity Gross Motor Function Assessment Tool
Bibliographic record
Abstract
Abstract Background: Use of standardized and scientifically sound outcome measures is highly encouraged in clinical practice and research. Researchers have identified that with the development of newer rehabilitation therapies we need technology-supported upper extremity outcome measures that are easily accessible and can measure change consistently and reliably. 3‐dimensional printing (3D-printing) has recently seen a meteoric rise in interest within medicine including the field of Physical Medicine and Rehabilitation. The primary objective of the current study was to evaluate the feasibility of designing and constructing a 3D printed version of the Toronto Rehabilitation Institute-Hand Function Test (TRI-HFT). The secondary objective was to assess the preliminary psychometrics of the 3D TRI-HFT in individuals with stroke. Results: 3D design files were created using the measurements of the original TRI-HFT objects. The 3D printed objects were then compared to the original test objects to ensure that the original dimensions were preserved. All objects were successfully printed except the sponge and paper which required some modification. The error margin for weight of the objects was within 10% for the rest of the objects. Nine participants underwent the following assessments: the Chedoke Arm and Hand Activity Inventory (CAHAI), Fugl Meyer Assessment-Hand (FMA-Hand), Chedoke McMaster stages of recovery of the arm (CMSA-Arm) and Chedoke McMaster stages of recovery of the hand (CMSA-Hand) and the 3D TRI-HFT for assessment of psychometric properties of the test. The video recorded assessment of the 3D TRI-HFT was reviewed by two assessors for reliability testing. Construct validity was assessed by comparing the scores on 3D TRI-HFT with the scores on CAHAI, CMSA-Arm, CMSA-Hand and FMA-Hand. The 3D TRI-HFT had high inter-rater reliability (ICC of 0.99; P < 0.000), high intra-rater reliability (ICC of 0.99; P < 0.000) and moderate to strong correlation with the CMSA-Arm, CMSA-Hand and FMA-Hand scores. Conclusions: The TRI-HFT could be successfully 3D printed and initial testing indicates that the test is a reliable and valid measure of upper extremity motor function in individuals with stroke.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".