Application of metrics constructed from vibrotactile thresholds to the assessment of tactile sensory changes in the hands
Bibliographic record
Abstract
Two tools for assessing tactile sensory disturbances in the hands have been constructed from mechanoreceptor-specific vibrotactile threshold shifts, and thresholds changes with time, and employed in a prospective study of forest workers (N=18). Statistically significant positive threshold shifts (i.e., reductions in sensitivity compared to the hands of healthy persons) were found in five hands at study inception (13.9%), and 15 hands at follow-up (41.7%). Four patterns of threshold shift could be identified, involving selectively the median and/or ulnar nerve pathways and/or end organs. Statistically significant positive threshold changes (i.e., reductions in sensitivity with time) were recorded in 69.4% of the hands over a five-year period, even though a majority of the workers remained symptom free. If the thresholds recorded from subjects not working with power tools are used to control for aging, lifestyle, and environmental factors during the five year period, then 40% of the remaining subjects are found to be experiencing work-related threshold changes in their hands. The ability of the threshold shift metric to predict the numbness reported by these subjects shows that it is closely associated with the tactile sensory changes occurring in their hands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".