Human factor ergonomic analysis of cervical cancer intracavitary therapy transport process based on Jack virtual simulation technology
Bibliographic record
Abstract
With the incidence of cervical cancer in women rising year by year, its impact on the physical and mental health of patients as well as their families is becoming more and more significant. At present, hospitals often use intracavitary radiation therapy, and intracavitary therapy transfer bed as a gynecological oncology intracavitary radiation medical auxiliary device plays an extremely important role. Therefore, for the design of intracavitary therapy transfer beds, the validation of man-machine adaptability can well help to optimize its structure. The paper firstly imports the three-dimensional model of intracavitary therapy transfer bed established in UG software into the ergonomics analysis software Jack; secondly, in the analysis environment, it establishes the human body model that conforms to the body dimensions of patients with cervical cancer as well as the human body model of healthcare workers; then, on this basis, it establishes the simulation model for the patient's posture of using the intracavitary therapy transfer bed as well as for different movements of the healthcare workers in the course of the treatment; finally, it analyzes the stresses of the patient's lower limbs, as well as the waist, back, and upper limbs of the healthcare workers through the simulation and analyzes the comfort of the patients and the healthcare personnel with respect to the different movements of the treatment task. The analysis of the simulation results can verify the rationality of the design of the intracavitary therapy transfer bed and the use of comfort, and at the same time, combined with the results of the analysis of the healthcare worker's working posture, to provide an analysis method for the structural optimization of the intracavitary therapy transfer bed.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".