Identification of Staphylococcus pseudintermedius Isolates from Wound Cultures by Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry Improves Accuracy of Susceptibility Reporting at an Increase in Cost
Bibliographic record
Abstract
< 0.0001]). Antibiotic susceptibility testing was performed in 42 isolates; none had an oxacillin MIC of 1.0 to 2.0 μg/ml, the range in which, if the isolate was misidentified as S. aureus, a very major error in susceptibility interpretation would occur. The increase in cost of the change in identification procedure was Can$17,558 per year in our laboratory, performing microbiology testing for community and acute-care patients in a zone servicing nearly 1.7 million people. While we will only continue to learn more about this emerging pathogen if we make attempts to properly identify it in clinical cultures, the additional time and cost involved may be unacceptably high in some laboratories. .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 0.003 |
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".