Needlestick injuries among electromyographers
Bibliographic record
Abstract
The objective of this study was to determine the self-reported prevalence of needlestick injuries among practicing electromyographers. In January 2008, an anonymous electronic survey was sent to all active members of the American Association for Neuromuscular and Electrodiagnostic Medicine (AANEM) who provided e-mail addresses to the Association. Eight hundred and eight members (56% neurologists, 43% physiatrists; 97% practicing physicians, 3% trainees) responded, with a response rate of 22% (808 of 3659). The mean number of years in practice, involving electromyography (EMG) at least 1 day per week, was 16 years. A majority of physicians (64%) reported at least one needlestick injury involving EMG, and 8% reported five or more injuries. Needlestick injuries involving patients with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), hepatitis B, and/or hepatitis C occurred in 1 of every 11 electromyographers. Nearly half of all respondents (44%) who experienced a needlestick injury stated that they did not report at least one injury event to official centers. Injuries were most likely to occur during a routine procedure (45%) or when a patient moved (26%). The most common preventable reason for injury was a perceived lack of time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".