Design of automatic urine collection system for medical system applications
Bibliographic record
Abstract
BACKGROUND: The results of urine tests are often affected by improper midstream urine collection time, urine spilling, and urine pollution, all of which can lead to an increase in the test error. OBJECTIVE: To solve this problem, aiming at improving the toilet environment at the hospitals and public physical examination centers, this paper designs an automatic urine collection system. It can automatically adjust the position of the urine cup with an infrared remote controller, or manually, adjust the position of the urine cup in special situations according to the needs of the user. It also has an alarm function. METHODS: The overall size and shape are designed based on the squatting pan, suitable for disposable plastic urine cups of different shapes and sizes. It can realize the automatic collection of midstream urine, manual collection in exceptional cases, emergency stops, and rescue calls. RESULTS: Through the trial survey, there was a significant difference between the statistical results of using the device and not using the device (t= 13.937, P= 0.000). 96% of the subjects thought that the design of the system was reasonable, 22% thought that it was inconvenient to use, and 91.7% of the medical staff thought that the system met the sampling requirements. CONCLUSIONS: Therefore, the trial evaluation is satisfactory, and the proposed collection system is suitable for use in hospitals at all levels and public health examination centers with a large amount of inspection.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".