Physical and composition characteristics of clinical secretions compared with test soils used for validation of flexible endoscope cleaning
Bibliographic record
Abstract
AIM: To determine which simulated-use test soils met the worst-case organic levels and viscosity of clinical secretions, and had the best adhesive characteristics. METHODS: Levels of protein, carbohydrate and haemoglobin, and vibrational viscosity of clinical endoscope secretions were compared with test soils including ATS, ATS2015, Edinburgh, Edinburgh-M (modified), Miles, 10% serum and coagulated whole blood. ASTM D3359 was used for adhesion testing. Cleaning of a single-channel flexible intubation endoscope was tested after simulated use. RESULTS: The worst-case levels of protein, carbohydrate and haemoglobin, and viscosity of clinical material were 219,828μg/mL, 9296μg/mL, 9562μg/mL and 6cP, respectively. Whole blood, ATS2015 and Edinburgh-M were pipettable with viscosities of 3.4cP, 9.0cP and 11.9cP, respectively. ATS2015 and Edinburgh-M best matched the worst-case clinical parameters, but ATS had the best adhesion with 7% removal (36.7% for Edinburgh-M). Edinburgh-M and ATS2015 showed similar soiling and removal characteristics from the surface and lumen of a flexible intubation endoscope. CONCLUSIONS: Of the test soils evaluated, ATS2015 and Edinburgh-M were found to be good choices for the simulated use of endoscopes, as their composition and viscosity most closely matched worst-case clinical material.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".