Coherent fluctuation nephelometry in clinical microbiology
Bibliographic record
Abstract
In this article data concerning coherent fluctuation nephelometry (CFN) use in clinical microbiology is presented. CFN-analyzer allows to solve two important problems – fast urine screening for bacteriuria within 2-4 hours and antibiotic susceptibility testing within 3-6 hours. Altogether more than 650 urine samples were tested, and the effectivity of CFN-analyzer for preliminary selection of samples for further analysis was shown. Method allows to detect negative samples, reducing the number of urine analyses by 70-80%. Simultaneous analysis of growth curves and concentration of microorganisms shows high sensitivity and specificity (95.2% и 96.9%). Also more than 250 antibiotic susceptibility tests were performed using CFN-analyzer to show its effectiveness for determination of resistant properties of both pure cultures and urine microflora without isolation of bacteria. The agreement with traditional methods was from 84% to 88%. The use of CFN-analyzer with express methods of identification of microorganisms (chromogenic nutrient broths or mass-spectrometry) allows to make full urine analysis within 1-2 days. In the future CFN-analyzer gives an opportunity to screen different human biological liquids, and finds an application for other microbiological tasks, including standardization and speeding-up in sanitary bacteriology.
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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 | 0.000 |
| 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".