Ergonomic wellness for the trainee in gastrointestinal endoscopy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Endoscopy-related injuries (ERIs) are prevalent in gastrointestinal endoscopy. The aim of this review is to address the growing concern of ERIs by evaluating the ergonomic risk factors and the efficacy of interventions and educational strategies aimed at mitigating these risks, including novel approaches. RECENT FINDINGS: ERIs are highly prevalent, exacerbated by factors such as repetitive strain, nonneutral postures, suboptimal equipment design, and the procedural learning curve. Female sex and smaller hand sizes have been identified as specific risk factors. Recent guidelines underscore the importance of ergonomic education and the integration of ergonomic principles into the foundational training of gastroenterology fellows. Advances in equipment design focus on adaptability to different hand sizes and ergonomic positions. Furthermore, the incorporation of microbreaks and macrobreaks, along with neutral monitor and bed positioning, has shown promise in reducing the incidence of ERIs. Wearable sensors may be helpful in monitoring and promoting ergonomic practices among trainees. SUMMARY: Ergonomic wellness is paramount for gastroenterology trainees to prevent ERIs and ensure a sustainable career. Effective strategies include ergonomic education integrated into curricula, equipment design improvements, and procedural adaptations such as scheduled breaks and optimal positioning. Sensor-based and camera-based systems may allow for education and feedback to be provided regarding ergonomics to trainees in the future.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".