Colorectal surgeon physical pain and conditioning: a national survey
Bibliographic record
Abstract
BACKGROUND: Workplace injuries are increasingly recognized as a substantial detriment to surgeon longevity and productivity. Limited data exist on pain and injury prevalence among rectal surgeons. In this epidemiologic study, we aimed to estimate the prevalence of physical discomfort among rectal surgeons in Canada and identify potential causative factors. METHODS: We distributed a web-based survey to rectal surgeons in Canada between January and October 2022. We included colorectal surgeons, surgical oncologists, and colorectal surgery fellows associated with Canadian university hospitals. RESULTS: Of the 72 surgeons we contacted, 48 participated (67%). More than 98% reported experiencing physical discomfort or pain during rectal surgery, with more than half experiencing these symptoms weekly. Neck, shoulders, and back were common pain or discomfort locations, whether surgeons were performing open surgery or using a minimally invasive platform. Laparoscopic equipment, headlight, and pelvic retractor use were the most common causes. Many surgeons (54%) sought professional treatment and employed risk-reducing strategies such as intraoperative stretching (48%) or after-work strength training exercises (52%). Satisfaction with pain levels during surgery was uncommon (40%). Multivariable analysis showed advancing age (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.02-1.23) and larger percentage of minimally invasive surgeries (OR 2.61, 95% CI 1.28-5.33) as significant predictors of increased discomfort. After-work exercise participation was protective in both open (OR 0.14, 95% CI 0.02-0.95) and minimally invasive surgeries (OR 0.60, 95% CI 0.37-0.98). CONCLUSION: Rectal surgeons in Canada commonly experience pain and injury during surgery, underscoring the need for improved safety measures to preserve their physical health and career longevity.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".