Evaluation of the Impact of Methicillin‐Resistant<i>Staphylococcus pseudintermedius</i>Biofilm Formation on Antimicrobial Susceptibility
Bibliographic record
Abstract
OBJECTIVE: To compare the minimum inhibitory concentration (MIC) of four antimicrobials in planktonic vs. biofilm-associated Staphylococcus pseudintermedius. STUDY DESIGN: In vitro study. SAMPLE POPULATION: 78 isolates from dogs colonized or infected with methicillin-resistant S. pseudintermedius (MRSP, n=39) or methicillin-susceptible S. pseudintermedius (MSSP, n=39). METHODS: Agar dilution was used to determine the MIC of amikacin, cefazolin, enrofloxacin, and gentamicin for planktonic bacteria. A modified broth microdilution assay was used to assess the MIC of biofilm-associated bacteria. RESULTS: MIC were significantly higher in biofilm-associated vs. planktonic bacteria for all antimicrobials; amikacin (median MIC: biofilm >2,000 μg/mL vs. planktonic 3 μg/mL, P<.0001), cefazolin (>1,000 vs. 0.5 μg/mL, P<.0001), enrofloxacin (>1,000 vs. 0.25 μg/mL, P<.0001), and gentamicin (>1,000 vs. 0.3 μg/mL, P<.001). For all antimicrobials, there were significant differences in planktonic MIC for MRSP and MSSP (all P<.0001) but no differences between biofilm MIC for MRSP and MSSP (P=.08-1.0). CONCLUSION: The MIC for biofilm-associated S. pseudintermedius are significantly higher than for planktonic bacteria. Standard methods for determining MIC are not appropriate for biofilm-associated infections. This must be considered when determining treatment regimens for infections that potentially involve biofilms, and further study of methods to control biofilm-associated infections is needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".