Design of an Automatic Jackson-Pratt Drain Auxiliary System for Postoperative Management Applications
Bibliographic record
Abstract
Background: After surgery, the exudation at a surgical site can cause complications and infections. The exudation is periodically removed through a Jackson-Pratt (JP) drain while maintaining a negative pressure in the JP bulb. However, the JP drainage tube may be clogged due to blood clotting. Thus, the periodic management of the JP drain is essential. In particular, the postoperative management of the JP drain includes squeezing and stripping it. In this study, we proposed a JP drain auxiliary system that can perform automatic stripping to assist specialists and nurses. Methodology: The proposed system was designed based on pre-experimental measurements and operated as a gripping and rolling device. Experimental Results: Twenty experiments were performed, and an average stripping efficiency of 93.8% was obtained. Conclusions: Consequently, we think that the proposed auxiliary system can automatically contribute to increase working efficiency for specialists and nurses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".