Phene Plate (PhP) biochemical fingerprinting
Bibliographic record
Abstract
Pulsed-field gel electrophoresis (PFGE) is currently considered the gold standard for genotyping of enterococci. However, PFGE is both expensive and time-consuming. The purpose of this study was to investigate whether the PhP system can be used as a reliable clinical screening method for detection of genetically related isolates of enterococci. If so, it should be possible to minimize the number of isolates subjected to PFGE typing, which would save time and money. Ninety-nine clinical enterococcal isolates were analysed by PhP (similarity levels 0.90-0.975) and PFGE (similarity levels < or =3 and < or =6 bands) and all possible pairs of isolates were cross-classified as matched or mismatched. We found that the probability that a pair of isolates (A and B) belonging to the same type according to PhP also belong to the same cluster according to PFGE, i.e. p(A(PFGE)=B(PFGE) * A(PhP)=B(PhP)), and the probability that a pair of isolates of different types according to PhP also belong to different clusters according to PFGE, i.e. p(A(PFGE) not equalB(PFGE) * A(PhP) not equalB(PhP)), was relatively high for E. faecalis (0.86 and 0.96, respectively), but was lower for E. faecium (0.51 and 0.77, respectively). The concordance which shows the probability that PhP and PFGE agree on match or mismatch was 86%-93% for E. faecalis and 54%-66% for E. faecium, which indicates that the PhP method may be useful for epidemiological typing of E. faecalis in the current settings but not for E. faecium.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".