Evaluation of <scp>d</scp> -Xylose and 1% Methyl-α- <scp>d</scp> -Glucopyranoside Fermentation Tests for Distinguishing <i>Enterococcus gallinarum</i> from <i>Enterococcus faecium</i>
Bibliographic record
Abstract
To determine the validity of the rapid xylose and methyl-alpha-D-glucopyranoside (MDG) fermentation tests in distinguishing Enterococcus gallinarum from Enterococcus faecium, 156 well-characterized clinical isolates of enterococci (55 E. gallinarum, 91 E. faecium, and 10 Enterococcus faecalis isolates) known to be of different clones were examined in a blinded fashion. Species identification was confirmed by PCR of the ddl ligase genes of E. faecium and E. faecalis and the vanC1 gene of E. gallinarum. Xylose tests were performed with D-xylose tablets by using a heavy bacterial suspension and were interpreted after 2 h of incubation. Standard MDG fermentation tests were read after 24 h of incubation. The xylose fermentation test had a sensitivity of 98% (54 of 55) and a specificity of 99% (100 of 101) in distinguishing E. gallinarum from E. faecium and E. faecalis. The standard MDG test had a sensitivity of 100% (55 of 55) and a specificity of 95% (96 of 101) after 24 h. The xylose fermentation test is a simple method, easily incorporated into laboratory protocols, that distinguishes E. gallinarum from E. faecium with high sensitivity and specificity in 2 h. The standard MDG test has high sensitivity and can be useful in ruling out the presence of E. gallinarum but requires overnight incubation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".