Musculoskeletal Injuries among ERCP Endoscopists in Canada
Bibliographic record
Abstract
BACKGROUND: There are few reports in the literature describing musculoskeletal complaints among endoscopists, and none are specific to those who perform endoscopic retrograde cholangiopancreatography (ERCP). PURPOSE: To examine the current practices of ERCP endoscopists and the prevalence of musculoskeletal injuries. METHODS: A self-report survey was sent to physicians practising ERCP across Canada identified through a pre-existing database. A second mailing was sent six weeks later to those who did not respond to the first mailing. RESULTS: Of 162 surveys sent, 122 responses were received, with five respondents indicating that they no longer performed ERCP and three declining to participate. Of the 114 participants, 67% reported at least one musculoskeletal complaint, and 58% reported two or more complaints. Seventy-four per cent attributed their symptoms to endoscopy and/or ERCP, and 79% reported that their condition was aggravated by performing ERCP. The most frequently reported pain symptoms were back pain (57%), neck pain (46%) and hand pain (33%), which are all consistent with the physical risks involved in performing ERCP. Only 51% reported taking regular breaks, and only 25% reported having fluoroscopy tables with adjustable heights. The room designs of the respondents' ERCP facilities were analyzed for ergonomic considerations: 67% had poor ergonomics and 33% had good ergonomics. Sixty-four per cent reported that they were interested in learning preventive strategies. CONCLUSIONS: Physicians who perform ERCP develop musculoskeletal injuries and are interested in learning about risk factor modification.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".