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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".