Bibliographic record
Abstract
Use of a computer at work does not appear to cause carpal tunnel syndrome, American researchers report. That is the somewhat surprising result of a recent study in Neurology (2001; 56:1568-70; www.neurology.org), in which researchers found that rates of carpal tunnel syndrome were no different among computer users than in the general population. “We wanted to do this study because conventional wisdom says that using a computer increases your risk of developing carpal tunnel syndrome,” explains Dr. J. Clarke Stevens, a neurologist at the Mayo Clinic in Scottsdale, Ariz. The assumption that computer use at work can cause the syndrome is deeply ingrained, as the Web sites of organizations like the Canadian Arthritis Society (www.arthritis.ca) and the Canadian Centre for Occupational Health and Safety (www.ccohs.ca) show. To test the theory, Stevens issued questionnaires to 257 frequent computer users at the Mayo Clinic in Scottsdale. The participants all used computers for an average of 6 hours per day, as well as having similar occupations and length of work experience. The questionnaire determined that 10.5% of respondents had symptoms that could be attributed to carpal tunnel syndrome. These respondents went into the lab to undergo nerve conduction tests to confirm the diagnosis. The tests found that only 9 of the original 257 respondents actually had the syndrome. This incidence of 3.5% is similar to the rate in the general population. “These percentages are similar to ones found in other studies looking at how often carpal tunnel [syndrome] occurs in the general population and not just among computer users,” says Stevens. Stevens stressed that computer use can still lead to problems. “There are a lot of aches and pains associated with computer use,” he says. “We just found that, at least in this group, frequent computer use doesn't seem to cause this syndrome.”
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".