Surgeon Strength: Ergonomics and Strength Training in Cardiothoracic Surgery
Bibliographic record
Abstract
With the high prevalence of musculoskeletal pain in surgeons and interventionalists, it is critical to analyze the impact of ergonomics on cardiothoracic surgeon health. Here, we review the existing literature and propose recommendations to improve physical preparedness for surgery both in and outside the operating room. For decades, cardiothoracic surgeons have suffered from musculoskeletal pain, most commonly in the neck, and back due to a lack of proper ergonomics during surgery. A lack of dedicated ergonomics curriculum during training may leave surgeons at a high predisposition for work-related musculoskeletal disorders. We searched PubMed, Google Scholar, and other sources for studies relevant to surgical ergonomics and prevalence of musculoskeletal disease among surgeons and interventionalists. Whenever possible, data from quantitative studies, and meta-analyses are presented. We also contacted experts and propose an exercise routine to improve physical preparedness for demands of surgery. To date, many studies have reported astonishingly high rates of work-related pain in surgeons with rates as high as 87% in minimally-invasive surgeons. Several optimizations regarding correct table height, monitor positioning, and loupe angles have been discussed. Lastly, implementation of ergonomics training at some programs have been effective at reducing the rates of musculoskeletal pain among surgeons. Surgical work-related stress injuries are more common than we think. Many factors including smaller incisions and technological advancements have led to this plight. Ultimately, work-related injuries are underreported and understudied and the field of surgical ergonomics remains open for investigative study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".