The Ergonomics Of Syringe Operation During The Injection Of Fluid Into Tissue Expanders
Bibliographic record
Abstract
The reconstruction process following a mastectomy procedure may require the use of a tissue expander(s) to accommodate an implant.Over several months, the expanders are routinely filled with sterile saline solution to gradually stretch the skin and muscle around the subpectoral pocket.The saline solution is injected manually by the surgeon or nurse into the expander using a syringe, catheter and needle.The injection process is physically demanding and repetitive; it has been reported to pose negative long-term effects on the muscles and joints of the hand, causing repetitive strain injuries for the syringe operator.Thus, the purpose of this study is to analyze the injection process and provide a comprehensive understanding of the factors that can be intervened upon to make this process more ergonomic and safe.To achieve this understanding, a laboratory testing setup is developed to mechanically simulate and analyze the fluid injection process into tissue expanders.Experimental results show that the magnitude of the syringe force required to inject the fluid is significantly correlated to the rate of compression, the size of the syringe and the resistance produced by the stretching of tissue expander.Moreover, the magnitude of forces measured during testing are found to be well above the recommended values to prevent repetitive stress injuries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".