Investigation of a New Hand Function Test Aimed at Discriminating Multi-layer Glove Dexterity
Bibliographic record
Abstract
Improved hand function (dexterity) tests are needed to better discriminate material and design differences in gloves that also limit the impact of human subject coordination on results. In this investigation, three specific tests were evaluated to determine their capability for measuring hand function as compared to subjective measurements made by study test subjects. Tests evaluated in the investigation included a modified pin pickup test based on European Norm 420, a modified pegboard test, and a two-point discriminator test. A series of seven different gloves, representing products used in hazardous materials and fire fighting applications were chosen for evaluation by five different test subjects. Detailed procedures were created to ensure consistent measurement of hand function. Test data were analyzed by comparing the measured hand function for the particular test to the average subjective ranking. From this analysis, the best correlation was demonstrated for the modified pegboard test that provided an overall correlation factor of 0.83 (based on a log function relation). Slightly poorer correlation was found for the modified pin pick-up test (at 0.74), while the two-point dicriminator test showed relatively poor correlation (0.56). Analysis of the modified pegboard test was also able to show discrimination (based on 95% confidence) between the majority of the glove types evaluated. As a result of this investigation, the modified pegboard test was proposed as a performance test in the evaluation of both hazardous materials response and fire fighting protective gloves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".