Comparisons of Musculoskeletal Disorders among Ten Different Medical Professions in Taiwan: A Nationwide, Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Medical personnel are at risk of musculoskeletal disorders but little is known whether the risk of musculoskeletal disorders were different among various medical professions. Therefore, this study compared the risk of musculoskeletal disorders among personnel of 10 different medical professions in Taiwan using a nationwide health claims database. METHODS: Data from the 2000-2010 Taiwan National Health Insurance Research Database were used to identify personnel of 10 different medical professions. Diagnoses based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) were used to identify eight different musculoskeletal disorders that occurred after the license issuance date. Cox proportional hazards model was used to compare the risk of eight musculoskeletal disorders among the 10 different medical professions using dentists as the reference category. RESULTS: A total of 7,820 medical personnel were included in the analysis. Using dentists as the reference category, physical therapists showed a significantly higher risk of all eight musculoskeletal disorders (ranging from 1.59 [p = 0.032] in sprains and strains of other and unspecified parts of back to 2.93 [p < 0.001] in spondylosis and allied disorders). CONCLUSIONS: Compared with dentists, a profession that already known to suffer from high rates of work-related musculoskeletal disorders, physical therapists, registered nurses, and doctors of Chinese medicine showed an even higher risk of musculoskeletal disorders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".