Comparison of the automated Phoenix with the Vitek 2 for the identification of <i>Streptococcus pneumoniae</i>Portions of this study were presented at the 2007 American Society of Microbiology 107th General Meeting in Toronto, Ontario, Canada.
Bibliographic record
Abstract
Rapid and accurate identification of Streptococcus pneumoniae is a critical component in the optimal management of infected patients. The performance of the BD Phoenix Automated Microbiology System (BD Diagnostic Systems, Sparks, Md.) was evaluated for identification of S. pneumoniae (n = 311) and was compared to the Vitek 2 (bioMérieux, Marcy l'Etoile, France). Strains with discordant identification between methods were resolved with 16S rRNA gene sequencing as the gold standard. The Phoenix and the Vitek 2 correctly identified 96.8% (n = 301) and 95.2% (n = 296) of S. pneumoniae strains, respectively. Overall, there was no statistically significant difference in the performance of the 2 automated systems for the identification of S. pneumoniae in this study. The Vitek 2 mean time-to-results for all streptococcal identification was 1.5 h faster than that for the Phoenix. We conclude that the automated Phoenix and the Vitek 2 systems are comparable in their ability to identify S. pneumoniae and are preferable to the use of routine biochemical assays, which have delayed time-to-results and are not dependably accurate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".