Musculoskeletal Injuries among ERCP Endoscopists in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it