Biofilm Formation by Group A Streptococci: Is There a Relationship with Treatment Failure?
Bibliographic record
Abstract
Group A streptococcus (GAS) is the primary cause of bacterial pharyngitis in children and adults. Up to one-third of patients treated for GAS pharyngitis fail to respond to antibiotic therapy. The objective of this cohort study was to evaluate GAS biofilm formation as a mechanism for antibiotic treatment failure using previously collected GAS isolates and penicillin treatment outcome data. The minimum biofilm eradication concentration (MBEC) assay device was used to determine the biofilm-forming capabilities, efficiencies, and antibiotic susceptibilities of GAS isolates. The MBECs and MICs of several antibiotics for GAS were determined. All 99 GAS isolates available for this study formed biofilms, with various efficiencies. Antibiotic MBECs were consistently higher than MICs for all of the GAS isolates. MBECs indicated penicillin insensitivity in 60% of GAS isolates, producing the first report of in vitro GAS insensitivity to penicillin. Using MBECs to predict penicillin treatment failure had better sensitivity (56%) but lower specificity (36%) than the sensitivity (0%) and specificity (100%) when MICs were used. However, the positive predictive value of the MBEC was superior to that of the MIC (56 versus 0%), while the negative predictive values (42 and 47%) were similar. More studies are needed to understand the roles of biofilms and the MBEC assay in predicting GAS treatment failure. In addition, further investigations are necessary to determine if non-biofilm-forming strains of GAS exist and the roles of in vivo monospecies and multispecies biofilms in streptococcal pharyngitis treatment failure.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".